def main():
parser = argparse.ArgumentParser()
parser.add_argument('gym_id', help="ID of the gym environment")
parser.add_argument('-a',
'--agent-config',
help="Agent configuration file")
parser.add_argument('-n',
'--network-spec',
default=None,
help="Network specification file")
parser.add_argument('-e',
'--episodes',
type=int,
default=50000,
help="Number of episodes")
parser.add_argument('-t',
'--max-timesteps',
type=int,
default=2000 * 60,
help="Maximum number of timesteps per episode")
# parser.add_argument('-m', '--monitor', help="Save results to this directory")
# parser.add_argument('-ms', '--monitor-safe', action='store_true', default=False, help="Do not overwrite previous results")
# parser.add_argument('-mv', '--monitor-video', type=int, default=0, help="Save video every x steps (0 = disabled)")
parser.add_argument('-s', '--save', help="Save agent to this dir")
parser.add_argument('-se',
'--save-episodes',
type=int,
default=100,
help="Save agent every x episodes")
parser.add_argument('-l', '--load', help="Load agent from this dir")
parser.add_argument('-D',
'--debug',
action='store_true',
default=False,
help="Show debug outputs")
args = parser.parse_args()
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
environment = OpenAIUniverse(args.gym_id)
environment.configure(remotes=1)
if args.agent_config is not None:
with open(args.agent_config, 'r') as fp:
agent_config = json.load(fp=fp)
else:
raise TensorForceError("No agent configuration provided.")
if args.network_spec:
with open(args.network_spec, 'r') as fp:
network_spec = json.load(fp=fp)
else:
network_spec = None
logger.info("No network configuration provided.")
agent = Agent.from_spec(spec=agent_config,
kwargs=dict(states=environment.states,
actions=environment.actions,
network=network_spec))
if args.load:
load_dir = os.path.dirname(args.load)
if not os.path.isdir(load_dir):
raise OSError(
"Could not load agent from {}: No such directory.".format(
load_dir))
agent.load_model(args.load)
if args.debug:
logger.info("-" * 16)
logger.info("Configuration:")
logger.info(agent_config)
runner = Runner(agent=agent, environment=environment, repeat_actions=1)
report_episodes = args.episodes // 1000
if args.debug:
report_episodes = 1
def episode_finished(r):
if r.episode % report_episodes == 0:
steps_per_second = r.timestep / (time.time() - r.start_time)
logger.info(
"Finished episode {} after {} timesteps. Steps Per Second {}".
format(r.episode, r.episode_timestep, steps_per_second))
logger.info("Episode reward: {}".format(r.episode_rewards[-1]))
logger.info("Average of last 500 rewards: {}".format(
sum(r.episode_rewards[-500:]) / 500))
logger.info("Average of last 100 rewards: {}".format(
sum(r.episode_rewards[-100:]) / 100))
return True
logger.info("Starting {agent} for Environment '{env}'".format(
agent=agent, env=environment))
runner.run(args.episodes,
args.max_timesteps,
episode_finished=episode_finished)
runner.close()
logger.info(
"Learning finished. Total episodes: {ep}".format(ep=runner.episode))
# if args.monitor:
# environment.gym.monitor.close()
environment.close()
global iter
global modelSaves
plt.pause(0.01)
if (iter == 30):
iter = 0
agent.save_model('longlongNoNorma/dense_mix')
modelSaves = modelSaves + 1
else:
iter = iter + 1
return True
# Start learning
runner.run(episodes=7000,
max_episode_timesteps=(candles.candle_nums + 100),
episode_finished=episode_finished)
#runner.run(episodes=1, max_episode_timesteps=(candles.candle_nums + 100), episode_finished=episode_finished, deterministic=True)
# Print statistics
print(
"Learning finished. Total episodes: {ep}. Average reward of last 100 episodes: {ar}."
.format(ep=runner.episode, ar=np.mean(runner.episode_rewards[-100:])))
print(env.pair_currency)
print(env.base_currency)
runner.close()
def main():
parser = argparse.ArgumentParser()
parser.add_argument('gym_id', help="Id of the Gym environment")
parser.add_argument('-a', '--agent', help="Agent configuration file")
parser.add_argument('-n',
'--network',
default=None,
help="Network specification file")
parser.add_argument('-e',
'--episodes',
type=int,
default=None,
help="Number of episodes")
parser.add_argument('-t',
'--timesteps',
type=int,
default=None,
help="Number of timesteps")
parser.add_argument('-m',
'--max-episode-timesteps',
type=int,
default=None,
help="Maximum number of timesteps per episode")
parser.add_argument('-d',
'--deterministic',
action='store_true',
default=False,
help="Choose actions deterministically")
parser.add_argument('-s', '--save', help="Save agent to this dir")
parser.add_argument('-se',
'--save-episodes',
type=int,
default=100,
help="Save agent every x episodes")
parser.add_argument('-l', '--load', help="Load agent from this dir")
parser.add_argument('--monitor', help="Save results to this directory")
parser.add_argument('--monitor-safe',
action='store_true',
default=False,
help="Do not overwrite previous results")
parser.add_argument('--monitor-video',
type=int,
default=0,
help="Save video every x steps (0 = disabled)")
parser.add_argument('--visualize',
action='store_true',
default=False,
help="Enable OpenAI Gym's visualization")
parser.add_argument('-D',
'--debug',
action='store_true',
default=False,
help="Show debug outputs")
parser.add_argument(
'--job',
type=str,
default=None,
help="For distributed mode: The job type of this agent.")
parser.add_argument(
'--task',
type=int,
default=0,
help="For distributed mode: The task index of this agent.")
args = parser.parse_args()
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__file__)
logger.setLevel(logging.INFO)
environment = OpenAIGym(gym_id=args.gym_id,
monitor=args.monitor,
monitor_safe=args.monitor_safe,
monitor_video=args.monitor_video,
visualize=args.visualize)
if args.agent is not None:
with open(args.agent, 'r') as fp:
agent = json.load(fp=fp)
else:
raise TensorForceError("No agent configuration provided.")
if args.network is not None:
with open(args.network, 'r') as fp:
network = json.load(fp=fp)
else:
network = None
logger.info("No network configuration provided.")
# TEST
agent["execution"] = dict(
type="distributed",
distributed_spec=dict(
job=args.job,
task_index=args.task,
# parameter_server=(args.job == "ps"),
cluster_spec=dict(ps=["192.168.2.107:22222"],
worker=["192.168.2.107:22223"
]))) if args.job else None
# END: TEST
agent = Agent.from_spec(spec=agent,
kwargs=dict(
states=environment.states,
actions=environment.actions,
network=network,
))
if args.load:
load_dir = os.path.dirname(args.load)
if not os.path.isdir(load_dir):
raise OSError(
"Could not load agent from {}: No such directory.".format(
load_dir))
agent.restore_model(args.load)
if args.save:
save_dir = os.path.dirname(args.save)
if not os.path.isdir(save_dir):
try:
os.mkdir(save_dir, 0o755)
except OSError:
raise OSError(
"Cannot save agent to dir {} ()".format(save_dir))
if args.debug:
logger.info("-" * 16)
logger.info("Configuration:")
logger.info(agent)
runner = Runner(agent=agent, environment=environment, repeat_actions=1)
if args.debug: # TODO: Timestep-based reporting
report_episodes = 1
else:
report_episodes = 100
logger.info("Starting {agent} for Environment '{env}'".format(
agent=agent, env=environment))
def episode_finished(r, id_):
if r.episode % report_episodes == 0:
steps_per_second = r.timestep / (time.time() - r.start_time)
logger.info(
"Finished episode {:d} after {:d} timesteps. Steps Per Second {:0.2f}"
.format(r.agent.episode, r.episode_timestep, steps_per_second))
logger.info("Episode reward: {}".format(r.episode_rewards[-1]))
if args.save and args.save_episodes is not None and not r.episode % args.save_episodes:
logger.info("Saving agent to {}".format(args.save))
r.agent.save_model(args.save)
return True
runner.run(num_timesteps=args.timesteps,
num_episodes=args.episodes,
max_episode_timesteps=args.max_episode_timesteps,
deterministic=args.deterministic,
episode_finished=episode_finished)
runner.close()
logger.info("Learning finished. Total episodes: {ep}".format(
ep=runner.agent.episode))
def main():
args = make_args_parser()
# print_config(args)
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
logger.addHandler(logging.StreamHandler(sys.stdout))
# # Temporary for quick access
# args.groups = 1
# args.run_all = False
#
# args.episodes = 501
# args.save_episodes = 3500
# args.testing = False
# args.target_group = 7
# args.restore_agent = True
#
# args.save_agent =./saved_model/" + path
# input_path = "./saved_model/" + "V3/group7-1000/"
# path = "V3/group7-1500/"
# output_path = "./outputs/" + path
# ~~~~~~~~~~~~~~~~~ Setting up the Model ~~~~~~~~~~~~~~~~~ #
# Initialize environment (tensorforce's template)
memory = {}
environment = ReJoin(
args.phase,
args.query,
args.episodes,
args.groups,
memory,
args.mode,
args.target_group,
args.run_all
)
if args.agent_config is not None:
with open(args.agent_config, "r") as fp:
agent_config = json.load(fp=fp)
else:
raise TensorForceError("No agent configuration provided.")
if args.network_spec is not None:
with open(args.network_spec, "r") as fp:
network_spec = json.load(fp=fp)
else:
raise TensorForceError("No network configuration provided.")
# Set up the PPO Agent
agent = Agent.from_spec(
spec=agent_config,
kwargs=dict(
states=environment.states, actions=environment.actions, network=network_spec,
variable_noise=0.5
),
)
if args.restore_agent != "":
agent.restore_model(directory=args.restore_agent)
runner = Runner(agent=agent, environment=environment)
# ~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~ #
report_episodes = 1
def episode_finished(r):
if r.episode % report_episodes == 0:
if args.save_agent != "" and args.testing is False and r.episode == args.save_episodes:
save_dir = os.path.dirname(args.save_agent)
if not os.path.isdir(save_dir):
try:
os.mkdir(save_dir, 0o755)
except OSError:
raise OSError("Cannot save agent to dir {} ()".format(save_dir))
r.agent.save_model(
directory=args.save_agent, append_timestep=True
)
logger.info(
"Episode {ep} reward: {r}".format(ep=r.episode, r=r.episode_rewards[-1])
)
logger.info(
"Average of last 100 rewards: {}\n".format(
sum(r.episode_rewards[-100:]) / 100
)
)
return True
logger.info(
"Starting {agent} for Environment '{env}'".format(agent=agent, env=environment)
)
# Start training or testing
runner.run(
episodes=args.episodes,
max_episode_timesteps=args.max_timesteps,
episode_finished=episode_finished,
deterministic=args.testing,
)
runner.close()
logger.info("Learning finished. Total episodes: {ep}".format(ep=runner.episode))
def find_convergence(eps):
last = eps[-1]
for i in range(1, len(eps)):
if eps[i * -1] != last:
print("Converged at episode:", len(eps) - i + 2)
return True
find_convergence(runner.episode_rewards)
# plt.figure(1)
# plt.hist(runner.episode_rewards)
#
# plt.figure(2)
# plt.plot(runner.episode_rewards, "b.", MarkerSize=2)
if not os.path.exists(args.outputs):
os.makedirs(args.outputs)
# Plot recorded costs over all episodes
# print(memory)
i = 2
for file, val in memory.items():
i += 1
plt.figure(i)
postgres_estimate = val["postgres_cost"]
costs = np.array(val["costs"])
max_val = max(costs)
min_val = min(costs)
plt.xlabel("episode")
plt.ylabel("cost")
plt.title(file)
plt.scatter(
np.arange(len(costs)),
costs,
c="g",
alpha=0.5,
marker=r"$\ast$",
label="Cost",
)
plt.legend(loc="upper right")
plt.scatter(
0,
[min_val],
c="r",
alpha=1,
marker=r"$\heartsuit$",
s=200,
label="min cost observed=" + str(min_val),
)
plt.scatter(
0,
[max_val],
c="b",
alpha=1,
marker=r"$\times$",
s=200,
label="max cost observed=" + str(max_val),
)
plt.legend(loc="upper right")
plt.scatter(
0,
[postgres_estimate],
c="c",
alpha=1,
marker=r"$\star$",
s=200,
label="postgreSQL estimate=" + str(postgres_estimate),
)
plt.legend(loc="upper right")
plt.savefig(args.outputs + file + ".png")
plt.show(block=True)
def main():
parser = argparse.ArgumentParser(description='Playground Flags.')
parser.add_argument('--game',
default='pommerman',
help='Game to choose.')
parser.add_argument('--config',
default='ffa_v0',
help='Configuration to execute.')
parser.add_argument('--agents',
default='tensorforce::ppo,test::agents.SimpleAgent,test::agents.SimpleAgent,test::agents.SimpleAgent',
help='Comma delineated list of agent types and docker locations to run the agents.')
parser.add_argument('--record_dir',
help="Directory to record the PNGs of the game. Doesn't record if None.")
args = parser.parse_args()
config = utility.AttrDict(getattr(configs, args.config)())
_agents = []
for agent_id, agent_info in enumerate(args.agents.split(",")):
agent = config.agent(agent_id, config.game_type)
agent_type, agent_control = agent_info.split("::")
assert agent_type in ["player", "random", "docker", "test", "tensorforce"]
if agent_type == "player":
assert agent_control in ["arrows"]
on_key_press, on_key_release = utility.get_key_control(agent_control)
agent = agents.PlayerAgent(
agent, utility.KEY_INPUT, on_key_press=on_key_press, on_key_release=on_key_release)
elif agent_type == "random":
agent = agents.RandomAgent(agent)
elif agent_type == "docker":
agent = agents.DockerAgent(
agent,
docker_image=agent_control,
docker_client=client,
port=agent_id+1000)
elif agent_type == "test":
agent = eval(agent_control)(agent)
elif agent_type == "tensorforce":
agent = agents.TensorForceAgent(agent, algorithm=agent_control)
training_agent = agent
_agents.append(agent)
gym.envs.registration.register(
id=config.env_id,
entry_point=config.env_entry_point,
kwargs=config.env_kwargs
)
env = config.env(**config.env_kwargs)
env.set_agents(_agents)
env.set_training_agent(training_agent.agent_id)
env.seed(0)
# Create a Proximal Policy Optimization agent
agent = training_agent.initialize(env)
atexit.register(functools.partial(clean_up_agents, _agents))
wrapped_env = WrappedEnv(env, visualize=True)
runner = Runner(agent=agent, environment=wrapped_env)
runner.run(episodes=10, max_episode_timesteps=2000)
print("Stats: ", runner.episode_rewards, runner.episode_timesteps, runner.episode_times)
try:
runner.close()
except AttributeError as e:
pass
def main():
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
console_handler = logging.StreamHandler()
console_handler.setFormatter(
logging.Formatter(
"%(asctime)s [%(threadName)-12.12s] [%(levelname)-5.5s] %(message)s"
))
logger.addHandler(console_handler)
parser = argparse.ArgumentParser()
parser.add_argument('-a',
'--agent-config',
help="Agent configuration file")
parser.add_argument('-n',
'--network-spec',
default=None,
help="Network specification file")
args = parser.parse_args()
if args.agent_config is not None:
with open(args.agent_config, 'r') as fp:
agent_config = json.load(fp=fp)
else:
raise TensorForceError("No agent configuration provided.")
if args.network_spec is not None:
with open(args.network_spec, 'r') as fp:
network_spec = json.load(fp=fp)
else:
network_spec = None
logger.info("No network configuration provided.")
if network_spec[0]['type'] == 'conv2d':
agent_config['states_preprocessing'] = [{
'type': 'expand_dims',
'axis': -1
}]
else:
agent_config['states_preprocessing'] = [{'type': 'flatten'}]
logger.info("Start training")
environment = Game2048()
agent = Agent.from_spec(spec=agent_config,
kwargs=dict(
states=environment.states,
actions=environment.actions,
network=network_spec,
))
runner = Runner(agent=agent, environment=environment, repeat_actions=1)
def episode_finished(r):
if r.episode % 100 == 0:
sps = r.timestep / (time.time() - r.start_time)
logger.info(
"Finished episode {ep} after {ts} timesteps. Steps Per Second {sps}"
.format(ep=r.episode, ts=r.timestep, sps=sps))
logger.info("Episode reward: {}".format(r.episode_rewards[-1]))
logger.info("Episode timesteps: {}".format(r.episode_timestep))
logger.info("Episode largest tile: {}".format(
r.environment.largest_tile))
logger.info("Average of last 500 rewards: {}".format(
sum(r.episode_rewards[-500:]) / 500))
logger.info("Average of last 100 rewards: {}".format(
sum(r.episode_rewards[-100:]) / 100))
return True
runner.run(timesteps=6000000,
episodes=1000,
max_episode_timesteps=10000,
deterministic=False,
episode_finished=episode_finished)
terminal = False
state = environment.reset()
while not terminal:
action = agent.act(state)
state, terminal, reward = environment.execute(action)
environment.print_state()
runner.close()
def main():
parser = argparse.ArgumentParser()
parser.add_argument('gym_id', help="Id of the Gym environment")
parser.add_argument('-i',
'--import-modules',
help="Import module(s) required for environment")
parser.add_argument('-a', '--agent', help="Agent configuration file")
parser.add_argument('-n',
'--network',
default=None,
help="Network specification file")
parser.add_argument('-e',
'--episodes',
type=int,
default=None,
help="Number of episodes")
parser.add_argument('-t',
'--timesteps',
type=int,
default=None,
help="Number of timesteps")
parser.add_argument('-m',
'--max-episode-timesteps',
type=int,
default=None,
help="Maximum number of timesteps per episode")
parser.add_argument('-d',
'--deterministic',
action='store_true',
default=False,
help="Choose actions deterministically")
parser.add_argument('-s', '--save', help="Save agent to this dir")
parser.add_argument('-se',
'--save-episodes',
type=int,
default=100,
help="Save agent every x episodes")
parser.add_argument('-l', '--load', help="Load agent from this dir")
parser.add_argument('--monitor', help="Save results to this directory")
parser.add_argument('--monitor-safe',
action='store_true',
default=False,
help="Do not overwrite previous results")
parser.add_argument('--monitor-video',
type=int,
default=0,
help="Save video every x steps (0 = disabled)")
parser.add_argument('--visualize',
action='store_true',
default=False,
help="Enable OpenAI Gym's visualization")
parser.add_argument('-D',
'--debug',
action='store_true',
default=False,
help="Show debug outputs")
parser.add_argument('-te',
'--test',
action='store_true',
default=False,
help="Test agent without learning.")
parser.add_argument(
'-sl',
'--sleep',
type=float,
default=None,
help=
"Slow down simulation by sleeping for x seconds (fractions allowed).")
parser.add_argument(
'--job',
type=str,
default=None,
help="For distributed mode: The job type of this agent.")
parser.add_argument(
'--task',
type=int,
default=0,
help="For distributed mode: The task index of this agent.")
args = parser.parse_args()
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger()
logger.setLevel(logging.INFO)
if args.import_modules is not None:
for module in args.import_modules.split(','):
importlib.import_module(name=module)
environment = OpenAIGym(gym_id=args.gym_id,
monitor=args.monitor,
monitor_safe=args.monitor_safe,
monitor_video=args.monitor_video,
visualize=args.visualize)
if args.agent is not None:
with open(args.agent, 'r') as fp:
agent = json.load(fp=fp)
else:
raise TensorForceError("No agent configuration provided.")
if args.network is not None:
with open(args.network, 'r') as fp:
network = json.load(fp=fp)
agent = Agent.from_spec(spec=agent,
kwargs=dict(states=environment.states,
actions=environment.actions,
network=network))
else:
logger.info("No network configuration provided.")
agent = Agent.from_spec(spec=agent,
kwargs=dict(states=environment.states,
actions=environment.actions))
if args.load:
load_dir = os.path.dirname(args.load)
if not os.path.isdir(load_dir):
raise OSError(
"Could not load agent from {}: No such directory.".format(
load_dir))
agent.restore_model(args.load)
if args.save:
save_dir = os.path.dirname(args.save)
if not os.path.isdir(save_dir):
try:
os.mkdir(save_dir, 0o755)
except OSError:
raise OSError(
"Cannot save agent to dir {} ()".format(save_dir))
if args.debug:
logger.info("-" * 16)
logger.info("Configuration:")
logger.info(agent)
runner = Runner(agent=agent, environment=environment, repeat_actions=1)
if args.debug: # TODO: Timestep-based reporting
report_episodes = 1
else:
report_episodes = 100
logger.info("Starting {agent} for Environment '{env}'".format(
agent=agent, env=environment))
def episode_finished(r, id_):
if r.episode % report_episodes == 0:
steps_per_second = r.timestep / (time.time() - r.start_time)
logger.info(
"Finished episode {:d} after {:d} timesteps. Steps Per Second {:0.2f}"
.format(r.agent.episode, r.episode_timestep, steps_per_second))
logger.info("Episode reward: {}".format(r.episode_rewards[-1]))
logger.info("Average of last 500 rewards: {:0.2f}".format(
sum(r.episode_rewards[-500:]) /
min(500, len(r.episode_rewards))))
logger.info("Average of last 100 rewards: {:0.2f}".format(
sum(r.episode_rewards[-100:]) /
min(100, len(r.episode_rewards))))
if args.save and args.save_episodes is not None and not r.episode % args.save_episodes:
logger.info("Saving agent to {}".format(args.save))
r.agent.save_model(args.save)
return True
runner.run(num_timesteps=args.timesteps,
num_episodes=args.episodes,
max_episode_timesteps=args.max_episode_timesteps,
deterministic=args.deterministic,
episode_finished=episode_finished,
testing=args.test,
sleep=args.sleep)
runner.close()
logger.info("Learning finished. Total episodes: {ep}".format(
ep=runner.agent.episode))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--gym_id',
default='CartPole-v0',
help="Name of the OpenAI Gym Environment")
parser.add_argument('-a',
'--agent',
type=str,
default='PPO',
help="Agent to train.")
parser.add_argument('-e',
'--episodes',
type=int,
default=20,
help="Number of episodes to train for.")
parser.add_argument('-t',
'--timesteps',
type=int,
default=None,
help="Number of timesteps to train for.")
parser.add_argument(
'-nv',
'--novisualize',
action='store_false',
default=True,
help="Don't visualize training (will speed up training)")
parser.add_argument('-m',
'--max-episode-timesteps',
type=int,
default=None,
help="Maximum number of timesteps per episode")
parser.add_argument(
'-d',
'--deterministic',
action='store_true',
default=False,
help="Choose deterministically and don't use random actions.")
parser.add_argument(
'-l',
'--load',
help="Load pretrained agent from this particular directory.")
parser.add_argument(
'-nm',
'--num-episodes-to-test',
type=int,
default=10,
help="Number of episodes to test the loaded policy for.")
parser.add_argument('-x',
'--exp',
type=str,
default='exp_test_delete_this',
help="Name of experiment for logging/saving weights.")
parser.add_argument('--monitor',
default='./logs/',
help="Save results and logs to this directory.")
parser.add_argument('--save',
default='./weights/',
help="Save trained model to this directory.")
parser.add_argument('--monitor-safe',
action='store_true',
default=False,
help="Do not overwrite previous results.")
parser.add_argument('--monitor-video',
type=int,
default=0,
help="Save video every x steps (0 = disabled).")
parser.add_argument('-D',
'--debug',
action='store_true',
default=False,
help="Show debug outputs.")
args = parser.parse_args()
# Load the required agent from custom module
logger.info('Loading {} Agent/Network'.format(args.agent))
if args.agent.lower() == 'ddpg':
from modules.custom_agents import DDPG_Agent_Network
agent, network = DDPG_Agent_Network()
elif args.agent.lower() == 'naf':
from modules.custom_agents import NAF_Agent_Network
agent, network = NAF_Agent_Network()
elif args.agent.lower() == 'trpo':
from modules.custom_agents import TRPO_Agent_Network
agent, network = TRPO_Agent_Network()
elif args.agent.lower() == 'ppo':
from modules.custom_agents import PPO_Agent_Network
agent, network = PPO_Agent_Network()
elif args.agent.lower() == 'vpg':
from modules.custom_agents import VPG_Agent_Network
agent, network = VPG_Agent_Network()
logfilepath = os.path.join(args.monitor, args.agent, args.exp)
if not args.load:
logger.info('Creating logging folder {}'.format(logfilepath))
os.system('mkdir -p {}'.format(logfilepath))
env = OpenAIGym(gym_id=args.gym_id,
monitor=logfilepath,
monitor_safe=args.monitor_safe,
monitor_video=args.monitor_video,
visualize=args.novisualize)
agent = Agent.from_spec(spec=agent,
kwargs=dict(
states=env.states,
actions=env.actions,
network=network,
))
if args.load:
logger.info("Testing pre-trained model!")
load_dir = os.path.dirname(args.load)
if not os.path.isdir(load_dir):
raise OSError(
"Could not load agent from {}: No such directory.".format(
load_dir))
agent.restore_model(args.load)
logger.info('Loaded pre-trained model weights!')
logger.info('Starting testing process!')
env = gym.make(args.gym_id)
for _i in range(args.num_episodes_to_test):
logger.info('Episode: {}'.format(_i))
s = env.reset()
done = False
while not done:
env.render()
action = agent.act(s)
s, r, done, _ = env.step(action)
# TODO: Make a logger here similar to episode_end()
return
def episode_finished(r, id):
if r.episode % report_episodes == 0:
steps_per_second = r.timestep / (time.time() - r.start_time)
logger.info(
"Finished episode {:d} after {:d} timesteps. Steps Per Second {:0.2f}"
.format(r.agent.episode, r.episode_timestep, steps_per_second))
logger.info("Episode reward: {}".format(r.episode_rewards[-1]))
logger.info("Average of last 500 rewards: {:0.2f}".format(
sum(r.episode_rewards[-500:]) /
min(500, len(r.episode_rewards))))
logger.info("Average of last 100 rewards: {:0.2f}".format(
sum(r.episode_rewards[-100:]) /
min(100, len(r.episode_rewards))))
return True
if args.debug:
logger.info("-" * 16)
logger.info("Configuration:")
logger.info(agent)
runner = Runner(agent=agent, environment=env, repeat_actions=1)
if args.debug: # TODO: Timestep-based reporting
report_episodes = 1
else:
report_episodes = 1
logger.info("Starting {agent} for Environment '{env}'".format(agent=agent,
env=env))
runner.run(num_timesteps=args.timesteps,
num_episodes=args.episodes,
max_episode_timesteps=args.max_episode_timesteps,
deterministic=args.deterministic,
episode_finished=episode_finished)
logger.info("Learning finished. Total episodes: {ep}".format(
ep=runner.agent.episode))
filepath = os.path.join(args.save, args.agent, args.exp)
logger.info('Creating directory {}'.format(filepath))
os.system('mkdir -p {}'.format(filepath)) # recursive mkdir
logger.info("Saving trained model to {}!".format(filepath))
filepath = agent.save_model(os.path.join(filepath, 'model'),
append_timestep=False)
logger.info("Saved trained model as: {}".format(filepath))
runner.close()
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
'-P',
'--port',
default=6025,
help=
"Port on which the UE4 Game listens on for incoming RL-client connections"
)
parser.add_argument('-H',
'--host',
default=None,
help="Hostname of the UE4 Game (default: localhost)")
parser.add_argument('-a',
'--agent-config',
help="Agent configuration file")
parser.add_argument('-n',
'--network-spec',
default=None,
help="Network specification file")
parser.add_argument('-e',
'--episodes',
type=int,
default=None,
help="Number of episodes")
parser.add_argument('-t',
'--timesteps',
type=int,
default=None,
help="Number of timesteps")
parser.add_argument('-m',
'--max-episode-timesteps',
type=int,
default=None,
help="Maximum number of timesteps per episode")
parser.add_argument('-d',
'--deterministic',
action='store_true',
default=False,
help="Choose actions deterministically")
parser.add_argument('-l', '--load', help="Load agent from this dir")
parser.add_argument('-D',
'--debug',
action='store_true',
default=False,
help="Show debug outputs")
parser.add_argument('-R',
'--random-test-run',
action="store_true",
help="Do a quick random test run on the env")
args = parser.parse_args()
# logging.basicConfig(filename="logfile.txt", level=logging.INFO)
logging.basicConfig(stream=sys.stderr, level=logging.DEBUG)
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
# We have to connect this remote env to get the specs.
# We also discretize axis-mappings b/c we will use a deep q-network.
# Use num_ticks==6 to match Nature paper by Mnih et al.
# ("human cannot press fire button with more than 10Hz", dt=1/60)
# TODO: Need to build in capturing and concat'ing last 4 images (plus 8-bit conversion!) into 1 input state signal.
# TODO: Use pre-processor for that.
environment = UE4Environment(host=args.host,
port=args.port,
connect=True,
discretize_actions=True,
num_ticks=6)
environment.seed(200)
# Do a quick random test-run with image capture of the first n images -> then exit after 1000 steps.
if args.random_test_run:
# Reset the env.
s = environment.reset()
img_format = "RGB" if len(environment.states["shape"]) == 3 else "L"
img = Image.fromarray(s, img_format)
# Save first received image as a sanity-check.
img.save("reset.png")
for i in range(1000):
s, is_terminal, r = environment.execute(action=random.choice(
range(environment.actions["num_actions"])))
if i < 10:
img = Image.fromarray(s, img_format)
img.save("{:03d}.png".format(i))
logging.debug("i={} r={} term={}".format(i, r, is_terminal))
if is_terminal:
environment.reset()
quit()
if args.agent_config is not None:
with open(args.agent_config, 'r') as fp:
agent_config = json.load(fp=fp)
else:
raise TensorForceError("No agent configuration provided.")
if args.network_spec is not None:
with open(args.network_spec, 'r') as fp:
network_spec = json.load(fp=fp)
else:
network_spec = None
logger.info("No network configuration provided.")
agent = Agent.from_spec(spec=agent_config,
kwargs=dict(states=environment.states,
actions=environment.actions,
network=network_spec))
if args.load:
load_dir = os.path.dirname(args.load)
if not os.path.isdir(load_dir):
raise OSError(
"Could not load agent from {}: No such directory.".format(
load_dir))
agent.restore_model(args.load)
if args.debug:
logger.info("-" * 16)
logger.info("Configuration:")
logger.info(agent_config)
runner = Runner(agent=agent, environment=environment, repeat_actions=1)
if args.debug: # TODO: Timestep-based reporting
report_episodes = 1
else:
report_episodes = 100
logger.info("Starting {agent} for Environment '{env}'".format(
agent=agent, env=environment))
def episode_finished(r, id_):
if r.episode % report_episodes == 0:
steps_per_second = r.global_timestep / (time.time() - r.start_time)
logger.info(
"Finished episode {} after {} timesteps. SPS={}".format(
r.global_episode, r.episode_timestep, steps_per_second))
logger.info("Episode reward: {}".format(r.episode_rewards[-1]))
logger.info("Average of last 500 rewards: {}".format(
sum(r.episode_rewards[-500:]) /
min(500, len(r.episode_rewards))))
logger.info("Average of last 100 rewards: {}".format(
sum(r.episode_rewards[-100:]) /
min(100, len(r.episode_rewards))))
return True
runner.run(timesteps=args.timesteps,
episodes=args.episodes,
max_episode_timesteps=args.max_episode_timesteps,
deterministic=args.deterministic,
episode_finished=episode_finished)
runner.close()
logger.info("Learning finished. Total episodes: {ep}".format(
ep=runner.agent.episode))
class Ezpeezy():
"""
This class is used to encompass all user behavior and interactions.
...
Attributes
----------
_env : tensorforce.Environment
custom environment used to define hyperparameter space and reward functions
_agent : tensorforce.Agent
dqn agent used to optimize the reward function defined in the environment
_runner : tensorforce.Runner
used to handle the training job of the agent
Methods
-------
set_k_folds(n_folds, pick_random=None)
Specifies to the environment what sort of cross-validation data
configuration to use.
train_on_data(X_train, y_train, X_valid=None, y_valid=None)
Specifies to the environment what data to use for training.
get_history()
Returns the history of the agent including the configurations it has already
tested.
run(num_episodes)
Begins using the agent to discover the actions required to optimize the
environment's reward.
"""
def __init__(self,
config,
model_fn,
model_type='sklearn',
model_train_batch_size=256,
model_train_epochs=75,
exploration=0.9,
exploration_decay_rate=0.8,
monitor_metric='val_loss',
opt='max',
starting_tol=-0.01,
tol_decay=0.5,
deepqn_lr=1e-15):
"""
Parameters
----------
config : dict
a dictionary representing the configuration of the hyperparameter space.
keys represent the name of the hyperparameter while keys can represent
ranges of the parameter space and its type
model_fn : function
function that returns the model you want to optimize
model_type : string
"sklearn" to signify that the passed in model_fn is of the sklearn library,
or "keras" to signify that the passed in model_fn is made from the keras library
model_train_batch_size : int
the batch size to use when training your model
model_train_epochs : int
number of eopchs to train your model for on each iteration
exploration : float
the agent's exploration value
exploration_decay_rate : float
the agent's exploration value's decay rate (uses exponential decay)
monitor_metric : None or string or function
the metric you would like to optimize in your model - string in the case of
model_type == 'keras', function if model_type == 'sklearn' or None if to use
the .score(X, y) function of the sklearn clasasifier
if function, defined to take in y_true, y_pred and return numeric type
opt : string
the optimization direction of the given monitor_metric
starting_tol : int/float
the value that you would like to see your metric to increase by at each
training step, or else end the agent's episode
tol_decay : int/float
at each training step in the episode, decrease the tolerance by this value
deepqn_lr : float
learning rate to use for the DQN
"""
self._env = CustomEnvironment(
config,
model_train_epoch=model_train_epochs,
model_train_batch_size=model_train_batch_size,
model_fn=model_fn,
model_type=model_type,
monitor_metric=monitor_metric,
opt=opt,
starting_tol=starting_tol,
tol_decay=tol_decay)
self._agent = DeepQNetwork(
states=self._env.states(),
actions=self._env.actions(),
max_episode_timesteps=self._env.max_episode_timesteps(),
memory=60,
batch_size=3,
exploration=dict(type='decaying',
unit='timesteps',
decay='exponential',
initial_value=exploration,
decay_steps=100000,
decay_rate=exploration_decay_rate),
discount=dict(type='decaying',
unit='timesteps',
decay='exponential',
initial_value=0.7,
decay_steps=100000,
decay_rate=0.5),
learning_rate=deepqn_lr)
self.runner = Runner(agent=self._agent, environment=self._env)
def set_k_folds(self, n_folds, pick_random=None):
"""
Specifies to the environment what sort of cross-validation data
configuration to use.
Parameters
----------
n_folds : int
the number of folds to divide your dataset into using k-fold
cross-validation
pick_random : int/None
if set to an int, randomly select pick_random of the n_folds to use
for training your model
"""
assert isinstance(n_folds, int), 'n_folds must be an int'
assert (isinstance(pick_random, int) & (pick_random < n_folds)) | (pick_random == None) , \
"pick random must be an int less than n_folds or None"
self._env.set_k_folds(n_folds, pick_random)
def train_on_data(self, X_train, y_train, X_valid=None, y_valid=None):
"""
Specifies to the environment what data to use for training.
Parameters
----------
X_train : iterible
data used to train your model
y_train : iterable
labels used to train your model
X_valid : iterable/None
data used to validate your model unless using k-fold CV
y_valid : iterable/None
labels used to validate your model unless using k-fold CV
"""
self._env.train_on_data(X_train, y_train, X_valid, y_valid)
def get_history(self):
"""
Returns the history of the agent including the configurations it has already
tested.
Returns
-------
pd.Dataframe
Dataframe representing each absolute time step with its episode, configuration
and monitored metric
"""
return self._env.get_history()
def run(self, num_episodes):
"""
Begins using the agent to discover the actions required to optimize the
environment's reward.
Parameters
----------
num_episodes : int
number of episodes to try your agent for on your environment
Prints
------
the best parameters for your goal.
"""
self._env.reset_history()
self._env.set_num_episodes(num_episodes)
self.runner.run(num_episodes=num_episodes)
print('Best parameters are:')
print(self._env.get_best_params())
self.runner.close()
def main():
# Print all possible environments in the Pommerman registry
# Instantiate the environment
DETERMINISTIC = False
VISUALIZE = False
if args.test:
DETERMINISTIC = True
VISUALIZE = True
config = ffa_competition_env()
env = Pomme(**config["env_kwargs"])
env.seed(0)
# Create a Proximal Policy Optimization agent
with open('ppo.json', 'r') as fp:
agent = json.load(fp=fp)
with open('mlp2_lstm_network.json', 'r') as fp:
network = json.load(fp=fp)
agent = Agent.from_spec(
spec=agent,
kwargs=dict(
states=dict(type='float', shape=env.observation_space.shape),
actions=dict(type='int', num_actions=env.action_space.n),
network=network
)
)
# Add 3 random agents
agents = []
for agent_id in range(3):
agents.append(SimpleAgent(config["agent"](agent_id, config["game_type"])))
# Add TensorforceAgent
agent_id += 1
agents.append(TensorforceAgent(config["agent"](agent_id, config["game_type"])))
env.set_agents(agents)
env.set_training_agent(agents[-1].agent_id)
env.set_init_game_state(None)
# Instantiate and run the environment for 5 episodes.
if VISUALIZE:
wrapped_env = WrappedEnv(env, True)
else:
wrapped_env = WrappedEnv(env)
runner = Runner(agent=agent, environment=wrapped_env)
rewards = []
episodes = []
def episode_finished(r):
nonlocal episodes
nonlocal rewards
print("Finished episode {ep} after {ts} timesteps (reward: {reward})".format(ep=r.episode, ts=r.episode_timestep,
reward=r.episode_rewards[-1]))
if r.episode % 1000 == 0:
agent.save_model(('./{}').format(EXPERIMENT_NAME), False)
try:
prev_data = pickle.load(open(EXPERIMENT_NAME, "rb"))
prev_len = len(prev_data[0])
prev_data[0].extend(rewards)
rewards = []
prev_data[1].extend(episodes)
episodes = []
pickle.dump(prev_data, open(EXPERIMENT_NAME, "wb"))
except (OSError, IOError) as e:
pickle.dump([rewards, episodes], open(EXPERIMENT_NAME, "wb"))
if r.episode_rewards[-1] >= 5:
print()
print()
print()
print("WINNER WINNER CHICKEN DINNER")
episodes.append(r.episode)
rewards.append(r.episode_rewards[-1])
return True
# Restore, Train, and Save Model
if args.test or args.resume: # If test, change settings and restore model
agent.restore_model('./','PPO_K_someS_500batch_biggerreward_99dis')
runner.run(episodes=EPISODES, max_episode_timesteps=2000, episode_finished=episode_finished, deterministic=False)
if not args.test:
agent.save_model(('./{}').format(EXPERIMENT_NAME), False)
print("Stats: ", runner.episode_rewards[-5:], runner.episode_timesteps[-5:])
#Dump reward values
try:
prev_data = pickle.load(open(EXPERIMENT_NAME, "rb"))
prev_len = len(prev_data[0])
prev_data[0].extend(rewards)
prev_data[1].extend(episodes)
print(episodes)
pickle.dump(prev_data, open(EXPERIMENT_NAME, "wb"))
except (OSError, IOError) as e:
pickle.dump([rewards, episodes], open(EXPERIMENT_NAME, "wb"))
try:
runner.close()
except AttributeError as e:
pass
def main():
parser = argparse.ArgumentParser(description="Playground Flags.")
parser.add_argument("--game",
default="pommerman",
help="Game to choose.")
parser.add_argument("--config",
default="PommeFFA-v0",
help="Configuration to execute. See env_ids in "
"configs.py for options.")
parser.add_argument("--agents",
default="tensorforce::ppo,test::agents.SimpleAgent,"
"test::agents.SimpleAgent,test::agents.SimpleAgent",
help="Comma delineated list of agent types and docker "
"locations to run the agents.")
parser.add_argument("--agent_env_vars",
help="Comma delineated list of agent environment vars "
"to pass to Docker. This is only for the Docker Agent."
" An example is '0:foo=bar:baz=lar,3:foo=lam', which "
"would send two arguments to Docker Agent 0 and one to"
" Docker Agent 3.",
default="")
parser.add_argument("--record_pngs_dir",
default=None,
help="Directory to record the PNGs of the game. "
"Doesn't record if None.")
parser.add_argument("--record_json_dir",
default=None,
help="Directory to record the JSON representations of "
"the game. Doesn't record if None.")
parser.add_argument("--render",
default=True,
help="Whether to render or not. Defaults to True.")
parser.add_argument("--game_state_file",
default=None,
help="File from which to load game state. Defaults to "
"None.")
args = parser.parse_args()
config = args.config
record_pngs_dir = args.record_pngs_dir
record_json_dir = args.record_json_dir
agent_env_vars = args.agent_env_vars
game_state_file = args.game_state_file
# TODO: After https://github.com/MultiAgentLearning/playground/pull/40
# this is still missing the docker_env_dict parsing for the agents.
agents = [
helpers.make_agent_from_string(agent_string, agent_id+1000)
for agent_id, agent_string in enumerate(args.agents.split(","))
]
env = make(config, agents, game_state_file)
training_agent = None
for agent in agents:
if type(agent) == TensorForceAgent:
training_agent = agent
env.set_training_agent(agent.agent_id)
break
if args.record_pngs_dir:
assert not os.path.isdir(args.record_pngs_dir)
os.makedirs(args.record_pngs_dir)
if args.record_json_dir:
assert not os.path.isdir(args.record_json_dir)
os.makedirs(args.record_json_dir)
# Create a Proximal Policy Optimization agent
agent = training_agent.initialize(env)
atexit.register(functools.partial(clean_up_agents, agents))
wrapped_env = WrappedEnv(env, visualize=args.render)
runner = Runner(agent=agent, environment=wrapped_env)
runner.run(episodes=10, max_episode_timesteps=2000)
print("Stats: ", runner.episode_rewards, runner.episode_timesteps,
runner.episode_times)
try:
runner.close()
except AttributeError as e:
pass
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-a', '--agent-config', help="Agent configuration file")
args = parser.parse_args()
#From quickstart on docs
#Network as list of layers
#This is from mlp2_embedding_network.json
network_spec = [
{
"type": "dense",
"size": 32
# "activation": "relu"
},
{
"type": "dense",
"size": 32
# "activation": "relu"
}
]
DATAPATH = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
observedFile = os.path.join(DATAPATH,r"prnio.int")
infoFile = os.path.join(DATAPATH,r"prnio.cfl")
environment = PycrysfmlEnvironment(observedFile, infoFile)
#get agent configuration
if args.agent_config is not None:
with open(args.agent_config, 'r') as fp:
agent_config = json.load(fp=fp)
else:
raise TensorForceError("No agent configuration provided.")
agent = Agent.from_spec(
spec=agent_config,
kwargs=dict(
states=environment.states,
actions=environment.actions,
network=network_spec,
)
)
#Use this line to resore a pre-trained agent
#agent.restore_model(file="/mnt/storage/deepQmodel_chisq")
runner = Runner(
agent=agent,
environment=environment,
repeat_actions=1
)
rewardsLog = []
steps = []
def episode_finished(r):
if r.episode % 10 == 0:
rewardsLog.append(r.episode_rewards[-1])
steps.append(r.episode)
if r.episode % 50 == 0:
sps = r.timestep / (time.time() - r.start_time)
file = open("/mnt/storage/trainingLog", "a")
file.write("Finished episode {ep} after {ts} timesteps. Steps Per Second {sps}\n".format(ep=r.episode,
ts=r.timestep,
sps=sps))
file.write("Episode reward: {}\n".format(r.episode_rewards[-1]))
file.write("Episode timesteps: {}\n".format(r.episode_timestep))
file.write("Average of last 500 rewards: {}\n".format(sum(r.episode_rewards[-500:]) / 500))
file.write("Average of last 100 rewards: {}\n".format(sum(r.episode_rewards[-100:]) / 100))
agent.save_model(directory="/mnt/storage/deepQmodel_simpleA_stdreward", append_timestep=False)
return True
runner.run(
timesteps=60000000,
episodes=5000,
max_episode_timesteps=1000,
deterministic=False,
episode_finished=episode_finished
)
#graph rewards
plt.scatter(steps, rewardsLog)
plt.savefig('/mnt/storage/rewardLog_simpleA_stdreward.png')
runner.close()
def compute(self, config_id, config, budget, working_directory):
if self.environment.max_episode_timesteps() is None:
min_capacity = 1000 + config['batch_size']
else:
min_capacity = self.environment.max_episode_timesteps(
) + config['batch_size']
max_capacity = 100000
capacity = min(
max_capacity,
max(min_capacity, config['memory'] * config['batch_size']))
frequency = max(16, int(config['frequency'] * config['batch_size']))
if config['ratio_based'] == 'yes':
ratio_based = True
clipping_value = config['clipping_value']
else:
ratio_based = False
clipping_value = 0.0
if config['baseline'] == 'no':
baseline_policy = None
baseline_objective = None
baseline_optimizer = None
estimate_horizon = False
estimate_terminal = False
estimate_advantage = False
else:
estimate_horizon = 'early'
estimate_terminal = True
estimate_advantage = (config['estimate_advantage'] == 'yes')
if config['baseline'] == 'same-policy':
baseline_policy = None
baseline_objective = None
baseline_optimizer = None
elif config['baseline'] == 'auto':
# other modes, shared network/policy etc !!!
baseline_policy = dict(
network=dict(type='auto', internal_rnn=False))
baseline_objective = dict(type='state_value',
huber_loss=0.0,
early_reduce=False)
baseline_optimizer = dict(
type='adam',
learning_rate=config['baseline_learning_rate'])
else:
assert False
if config['entropy_regularization'] < 3e-5: # yes/no better
entropy_regularization = 0.0
else:
entropy_regularization = config['entropy_regularization']
agent = dict(
agent='tensorforce',
policy=dict(network=dict(type='auto', internal_rnn=False)),
memory=dict(type='replay', capacity=capacity),
update=dict(unit='timesteps',
batch_size=config['batch_size'],
frequency=frequency),
optimizer=dict(type='adam', learning_rate=config['learning_rate']),
objective=dict(type='policy_gradient',
ratio_based=ratio_based,
clipping_value=clipping_value,
early_reduce=False),
reward_estimation=dict(horizon=config['horizon'],
discount=config['discount'],
estimate_horizon=estimate_horizon,
estimate_actions=False,
estimate_terminal=estimate_terminal,
estimate_advantage=estimate_advantage),
baseline_policy=baseline_policy,
baseline_objective=baseline_objective,
baseline_optimizer=baseline_optimizer,
preprocessing=None,
l2_regularization=0.0,
entropy_regularization=entropy_regularization)
# num_episodes = list()
final_reward = list()
max_reward = list()
rewards = list()
for n in range(round(budget)):
runner = Runner(agent=agent, environment=self.environment)
# performance_threshold = runner.environment.max_episode_timesteps() - agent['reward_estimation']['horizon']
# def callback(r, p):
# return True
runner.run(num_episodes=500, use_tqdm=False)
runner.close()
# num_episodes.append(len(runner.episode_rewards))
final_reward.append(
float(np.mean(runner.episode_rewards[-20:], axis=0)))
average_rewards = [
float(np.mean(runner.episode_rewards[n:n + 20], axis=0))
for n in range(len(runner.episode_rewards) - 20)
]
max_reward.append(float(np.amax(average_rewards, axis=0)))
rewards.append(list(runner.episode_rewards))
# mean_num_episodes = float(np.mean(num_episodes, axis=0))
mean_final_reward = float(np.mean(final_reward, axis=0))
mean_max_reward = float(np.mean(max_reward, axis=0))
# loss = mean_num_episodes - mean_final_reward - mean_max_reward
loss = -mean_final_reward - mean_max_reward
return dict(loss=loss, info=dict(rewards=rewards))
def main():
env = OpenAIGym("P3DX-v0")
agent = DQNAgent(
states=dict(type='float', shape=(80,80,4)),
actions=dict(type='int', num_actions=7),
network= [
dict(
type="conv2d",
size=16,
window=[8,8],
stride=4,
activation="relu"
),
dict(
type="conv2d",
size=32,
window=[4,4],
stride=2,
activation="relu"
),
dict(
type="flatten"
),
dict(
type="dense",
size=256
)
],
actions_exploration = dict(
type="epsilon_decay",
initial_epsilon=1.0,
final_epsilon=0.1,
timesteps=1000
),
memory=dict(
type="replay",
capacity=1000,
include_next_states=True
),
update_mode = dict(
unit="timesteps",
batch_size=16,
frequency=4
),
discount = 0.99,
entropy_regularization = None,
double_q_model = True,
optimizer = dict(
type="adam",
learning_rate=1e-4
)
)
try:
agent.restore_model(directory="data/", file="data-117246")
print("Found data!")
except:
print("Can't load data")
SAVE_INTERVAL = 10
def episode_finished(r):
#print(r.episode)
if r.episode % SAVE_INTERVAL == 0:
print("Finished episode {ep} after {ts} timesteps".format(ep=r.episode + 1, ts=r.timestep + 1))
print("Episode reward: {}".format(r.episode_rewards[-1]))
print("Average of last {} rewards: {}\n".format(SAVE_INTERVAL, np.mean(r.episode_rewards[-SAVE_INTERVAL:])))
r.agent.save_model(directory="data/data", append_timestep=True)
with open("reward_history.csv", "a") as csvfile:
writer = csv.writer(csvfile)
for reward in r.episode_rewards[-SAVE_INTERVAL:]:
writer.writerow([r.episode, reward])
with open("episode_history.csv", "a") as csvfile:
writer = csv.writer(csvfile)
writer.writerow([r.episode, r.timestep])
'''
with open("individual_reward_history.csv", "a") as csvfile:
writer = csv.writer(csvfile)
writer.writerow([r.episode, r.episode_rewards[-1]])
'''
return True
runner = Runner(
agent = agent, # Agent object
environment = env # Environment object
)
max_episodes = 10000
max_timesteps = 50000000
runner.run(max_timesteps,max_episodes, episode_finished=episode_finished)
runner.close()
def main():
'''CLI interface to bootstrap taining'''
parser = argparse.ArgumentParser(description="Playground Flags.")
parser.add_argument("--game", default="pommerman", help="Game to choose.")
parser.add_argument("--config",
default="PommeFFACompetition-v0",
help="Configuration to execute. See env_ids in "
"configs.py for options.")
parser.add_argument("--agents",
default="tensorforce::ppo,test::agents.SimpleAgent,"
"test::agents.SimpleAgent,test::agents.SimpleAgent",
help="Comma delineated list of agent types and docker "
"locations to run the agents.")
parser.add_argument("--agent_env_vars",
help="Comma delineated list of agent environment vars "
"to pass to Docker. This is only for the Docker Agent."
" An example is '0:foo=bar:baz=lar,3:foo=lam', which "
"would send two arguments to Docker Agent 0 and one to"
" Docker Agent 3.",
default="")
parser.add_argument("--record_pngs_dir",
default=None,
help="Directory to record the PNGs of the game. "
"Doesn't record if None.")
parser.add_argument("--record_json_dir",
default=None,
help="Directory to record the JSON representations of "
"the game. Doesn't record if None.")
parser.add_argument("--render",
default=False,
action='store_true',
help="Whether to render or not. Defaults to False.")
parser.add_argument("--game_state_file",
default=None,
help="File from which to load game state. Defaults to "
"None.")
parser.add_argument("--model_save_dir",
default="./ppo_model/model",
help="Directory to save the learnt models.")
args = parser.parse_args()
config = args.config
record_pngs_dir = args.record_pngs_dir
record_json_dir = args.record_json_dir
agent_env_vars = args.agent_env_vars
game_state_file = args.game_state_file
# TODO: After https://github.com/MultiAgentLearning/playground/pull/40
# this is still missing the docker_env_dict parsing for the agents.
agents = [
helpers.make_agent_from_string(agent_string, agent_id + 1000)
for agent_id, agent_string in enumerate(args.agents.split(","))
]
env = make(config, agents, game_state_file)
training_agent = None
for agent in agents:
# if type(agent) == TensorForceAgent:
if agent.trainable:
training_agent = agent
env.set_training_agent(agent.agent_id)
break
if args.record_pngs_dir:
assert not os.path.isdir(args.record_pngs_dir)
os.makedirs(args.record_pngs_dir)
if args.record_json_dir:
assert not os.path.isdir(args.record_json_dir)
os.makedirs(args.record_json_dir)
# Create a Proximal Policy Optimization agent
agent = training_agent.initialize(env)
atexit.register(functools.partial(clean_up_agents, agents))
wrapped_env = WrappedEnv(env, visualize=args.render)
runner = Runner(agent=agent, environment=wrapped_env)
num_epi = 200000
vis_epi = 100
max_reward = -10.0
for i in range(num_epi // vis_epi):
runner.run(episodes=vis_epi, max_episode_timesteps=2000)
m_reward = np.mean(runner.episode_rewards[-vis_epi:])
m_step = np.mean(runner.episode_timesteps[-vis_epi:])
m_time = np.mean(runner.episode_times[-vis_epi:])
print("[Iter %s]: %.3f %.3f %.3f" % (i, m_reward, m_step, m_time))
sys.stdout.flush()
if m_reward > max_reward:
max_reward = m_reward
agent.save_model(args.model_save_dir, False)
print("[Save] max_reward=%s" % (max_reward))
try:
runner.close()
except AttributeError as e:
pass
def main():
parser = argparse.ArgumentParser()
parser.add_argument('gym_id', help="Id of the Gym environment")
parser.add_argument('-a', '--agent', help="Agent configuration file")
parser.add_argument('-n', '--network', default=None, help="Network specification file")
parser.add_argument('-e', '--episodes', type=int, default=None, help="Number of episodes")
parser.add_argument('-t', '--timesteps', type=int, default=None, help="Number of timesteps")
parser.add_argument('-m', '--max-episode-timesteps', type=int, default=None, help="Maximum number of timesteps per episode")
parser.add_argument('-d', '--deterministic', action='store_true', help="Choose actions deterministically")
parser.add_argument('-M', '--mode', choices=('tmux', 'child'), default='tmux', help="Starter mode")
parser.add_argument('-W', '--num-workers', type=int, default=1, help="Number of worker agents")
parser.add_argument('-C', '--child', action='store_true', help="Child process")
parser.add_argument('-P', '--parameter-server', action='store_true', help="Parameter server")
parser.add_argument('-I', '--task-index', type=int, default=0, help="Task index")
parser.add_argument('-K', '--kill', action='store_true', help="Kill runners")
parser.add_argument('-L', '--logdir', default='logs_async', help="Log directory")
parser.add_argument('-D', '--debug', action='store_true', help="Show debug outputs")
args = parser.parse_args()
session_name = 'OpenAI-' + args.gym_id
shell = '/bin/bash'
kill_cmds = [
"kill $( lsof -i:12222-{} -t ) > /dev/null 2>&1".format(12222 + args.num_workers),
"tmux kill-session -t {}".format(session_name),
]
if args.kill:
os.system("\n".join(kill_cmds))
return 0
if not args.child:
# start up child processes
target_script = os.path.abspath(inspect.stack()[0][1])
def wrap_cmd(session, name, cmd):
if isinstance(cmd, list):
cmd = ' '.join(shlex_quote(str(arg)) for arg in cmd)
if args.mode == 'tmux':
return 'tmux send-keys -t {}:{} {} Enter'.format(session, name, shlex_quote(cmd))
elif args.mode == 'child':
return '{} > {}/{}.{}.out 2>&1 & echo kill $! >> {}/kill.sh'.format(
cmd, args.logdir, session, name, args.logdir
)
def build_cmd(ps, index):
cmd_args = [
'CUDA_VISIBLE_DEVICES=',
sys.executable, target_script,
args.gym_id,
'--agent', os.path.join(os.getcwd(), args.agent),
'--network', os.path.join(os.getcwd(), args.network),
'--num-workers', args.num_workers,
'--child',
'--task-index', index
]
if args.episodes is not None:
cmd_args.append('--episodes')
cmd_args.append(args.episodes)
if args.timesteps is not None:
cmd_args.append('--timesteps')
cmd_args.append(args.timesteps)
if args.max_episode_timesteps is not None:
cmd_args.append('--max-episode-timesteps')
cmd_args.append(args.max_episode_timesteps)
if args.deterministic:
cmd_args.append('--deterministic')
if ps:
cmd_args.append('--parameter-server')
if args.debug:
cmd_args.append('--debug')
return cmd_args
if args.mode == 'tmux':
cmds = kill_cmds + ['tmux new-session -d -s {} -n ps'.format(session_name)]
elif args.mode == 'child':
cmds = ['mkdir -p {}'.format(args.logdir),
'rm -f {}/kill.sh'.format(args.logdir),
'echo "#/bin/bash" > {}/kill.sh'.format(args.logdir),
'chmod +x {}/kill.sh'.format(args.logdir)]
cmds.append(wrap_cmd(session_name, 'ps', build_cmd(ps=True, index=0)))
for i in xrange(args.num_workers):
name = 'worker{}'.format(i)
if args.mode == 'tmux':
cmds.append('tmux new-window -t {} -n {} -d {}'.format(session_name, name, shell))
cmds.append(wrap_cmd(session_name, name, build_cmd(ps=False, index=i)))
# add one PS call
# cmds.append('tmux new-window -t {} -n ps -d {}'.format(session_name, shell))
print("\n".join(cmds))
os.system("\n".join(cmds))
return 0
ps_hosts = ['127.0.0.1:{}'.format(12222)]
worker_hosts = []
port = 12223
for _ in range(args.num_workers):
worker_hosts.append('127.0.0.1:{}'.format(port))
port += 1
cluster = {'ps': ps_hosts, 'worker': worker_hosts}
cluster_spec = tf.train.ClusterSpec(cluster)
environment = OpenAIGym(args.gym_id)
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO) # log_levels[agent.log_level])
if args.agent is not None:
with open(args.agent, 'r') as fp:
agent = json.load(fp=fp)
else:
raise TensorForceError("No agent configuration provided.")
if args.network is not None:
with open(args.network, 'r') as fp:
network = json.load(fp=fp)
else:
network = None
logger.info("No network configuration provided.")
if args.parameter_server:
agent['device'] = '/job:ps/task:{}'.format(args.task_index) # '/cpu:0'
else:
agent['device'] = '/job:worker/task:{}'.format(args.task_index) # '/cpu:0'
agent['distributed'] = dict(
cluster_spec=cluster_spec,
task_index=args.task_index,
parameter_server=args.parameter_server
)
agent = Agent.from_spec(
spec=agent,
kwargs=dict(
states=environment.states,
actions=environment.actions,
network=network
)
)
logger.info("Starting distributed agent for OpenAI Gym '{gym_id}'".format(gym_id=args.gym_id))
logger.info("Config:")
logger.info(agent)
runner = Runner(
agent=agent,
environment=environment,
repeat_actions=1
)
if args.debug: # TODO: Timestep-based reporting
report_episodes = 1
else:
report_episodes = 100
def episode_finished(r):
if r.episode % report_episodes == 0:
steps_per_second = r.timestep / (time.time() - r.start_time)
logger.info("Finished episode {} after overall {} timesteps. Steps Per Second {}".format(
r.agent.episode,
r.agent.timestep,
steps_per_second)
)
logger.info("Episode reward: {}".format(r.episode_rewards[-1]))
logger.info("Average of last 500 rewards: {}".format(sum(r.episode_rewards[-500:]) / min(500, len(r.episode_rewards))))
logger.info("Average of last 100 rewards: {}".format(sum(r.episode_rewards[-100:]) / min(100, len(r.episode_rewards))))
return True
runner.run(
timesteps=args.timesteps,
episodes=args.episodes,
max_episode_timesteps=args.max_episode_timesteps,
deterministic=args.deterministic,
episode_finished=episode_finished
)
runner.close()
def compute(self, config_id, config, budget, working_directory):
if self.environment.max_episode_timesteps() == None:
min_capacity = 1000 + config['batch_size']
else:
min_capacity = self.environment.max_episode_timesteps() + config['batch_size']
max_capacity = 100000
capacity = min(max_capacity, max(min_capacity, config['memory'] * config['batch_size']))
frequency = max(4, int(config['frequency'] * config['batch_size']))
if config['baseline'] == 'no':
baseline_policy = None
baseline_objective = None
baseline_optimizer = None
estimate_horizon = False
estimate_terminal = False
estimate_advantage = False
else:
estimate_horizon = 'late'
estimate_advantage = (config['estimate_advantage'] == 'yes')
if config['baseline'] == 'same-policy':
baseline_policy = None
baseline_objective = None
baseline_optimizer = None
elif config['baseline'] == 'auto':
# other modes, shared network/policy etc !!!
baseline_policy = dict(network=dict(type='auto', internal_rnn=False))
baseline_objective = dict(
type='value', value='state', huber_loss=0.0, early_reduce=False
)
baseline_optimizer = dict(
type='adam', learning_rate=config['baseline_learning_rate']
)
else:
assert False
if config['l2_regularization'] < 3e-5: # yes/no better
l2_regularization = 0.0
else:
l2_regularization = config['l2_regularization']
if config['entropy_regularization'] < 3e-5: # yes/no better
entropy_regularization = 0.0
else:
entropy_regularization = config['entropy_regularization']
# Set agent configuration according to configspace
print("### Set agent configuration according to configspace")
agent = dict(
agent='tensorforce',
policy=dict(network=dict(type='auto', internal_rnn=False)),
memory=dict(type='replay', capacity=capacity), # replay, recent
update=dict(unit='timesteps', batch_size=config['batch_size'], frequency=frequency), # timesteps, episode
optimizer=dict(type='adam', learning_rate=config['learning_rate']),
objective=dict(
type='policy_gradient', ratio_based=True, clipping_value=0.1,
early_reduce=False
),
reward_estimation=dict(
horizon=config['horizon'], discount=config['discount'],
estimate_horizon=estimate_horizon, estimate_actions=False,
estimate_terminal=False, estimate_advantage=estimate_advantage
),
baseline_policy=baseline_policy, baseline_objective=baseline_objective,
baseline_optimizer=baseline_optimizer,
preprocessing=None,
l2_regularization=l2_regularization, entropy_regularization=entropy_regularization
)
# Set state representation according to configspace
print("### Set state representation according to configspace")
# Example state configurations to evaluate
config_state = None
if config['state'] == 0:
config_state = []
elif config['state'] == 1:
config_state = ['bin_buffer_fill']
elif config['state'] == 2:
config_state = ['bin_buffer_fill', 'distance_to_action']
elif config['state'] == 3:
config_state = ['bin_buffer_fill', 'distance_to_action', 'bin_machine_failure']
elif config['state'] == 4:
config_state = ['bin_buffer_fill', 'distance_to_action', 'bin_machine_failure', 'order_waiting_time']
self.environment.environment.parameters.update({'TRANSP_AGENT_STATE': config_state})
self.environment.environment.parameters.update({'TRANSP_AGENT_REWARD': config['reward']})
#self.environment.environment.parameters.update({'TRANSP_AGENT_REWARD_INVALID_ACTION': config['reward_invalid']})
#self.environment.environment.parameters.update({'TRANSP_AGENT_REWARD_OBJECTIVE_WEIGHTS': config['reward_weighted']})
self.environment.environment.parameters.update({'TRANSP_AGENT_MAX_INVALID_ACTIONS': config['max_invalid_actions']})
self.environment.environment.parameters.update({'TRANSP_AGENT_WAITING_TIME_ACTION': config['waiting_if_invalid_actions']})
# num_episodes = list()
final_reward = list()
max_reward = list()
rewards = list()
for n in range(round(budget)):
runner = Runner(agent=agent, environment=self.environment)
#runner = Runner(agent='config/ppo2.json', environment=self.environment)
# performance_threshold = runner.environment.max_episode_timesteps() - agent['reward_estimation']['horizon']
# def callback(r, p):
# return True
runner.run(num_episodes=NUM_EPISODES, use_tqdm=False)
runner.close()
# num_episodes.append(len(runner.episode_rewards))
final_reward.append(float(np.mean(runner.episode_rewards[-20:], axis=0)))
average_rewards = [
float(np.mean(runner.episode_rewards[n: n + 20], axis=0))
for n in range(len(runner.episode_rewards) - 20)
]
max_reward.append(float(np.amax(average_rewards, axis=0)))
rewards.append(list(runner.episode_rewards))
# mean_num_episodes = float(np.mean(num_episodes, axis=0))
mean_final_reward = float(np.mean(final_reward, axis=0))
mean_max_reward = float(np.mean(max_reward, axis=0))
# loss = mean_num_episodes - mean_final_reward - mean_max_reward
loss = -mean_final_reward - mean_max_reward
return dict(loss=loss, info=dict(rewards=rewards))
def test_blogpost_introduction_runner(self):
from .minimal_test import MinimalTest
from tensorforce.agents import DQNAgent
from tensorforce.execution import Runner
environment = MinimalTest(specification={'int': ()})
network_spec = [dict(type='dense', size=32)]
agent = DQNAgent(states=environment.states,
actions=environment.actions,
network=network_spec,
memory=dict(type='replay',
include_next_states=True,
capacity=100),
target_sync_frequency=50)
runner = Runner(agent=agent, environment=environment)
def episode_finished(runner):
if runner.episode % 100 == 0:
print(sum(runner.episode_rewards[-100:]) / 100)
return runner.episode < 100 \
or not all(reward >= 1.0 for reward in runner.episode_rewards[-100:])
# runner.run(episodes=1000, episode_finished=episode_finished)
runner.run(episodes=10, episode_finished=episode_finished
) # Only 10 episodes for this test
runner.close()
### Code block: next
agent = DQNAgent(states=environment.states,
actions=environment.actions,
network=network_spec,
memory=dict(type='replay',
include_next_states=True,
capacity=100),
target_sync_frequency=50)
# max_episodes = 1000
max_episodes = 10 # Only 10 episodes for this test
max_timesteps = 2000
episode = 0
episode_rewards = list()
while True:
state = environment.reset()
agent.reset()
timestep = 0
episode_reward = 0
while True:
action = agent.act(states=state)
state, terminal, reward = environment.execute(action=action)
agent.observe(terminal=terminal, reward=reward)
timestep += 1
episode_reward += reward
if terminal or timestep == max_timesteps:
break
episode += 1
episode_rewards.append(episode_reward)
if all(reward >= 1.0 for reward in
episode_rewards[-100:]) or episode == max_episodes:
break
agent.close()
environment.close()
class TensorforceTradingStrategy(TradingStrategy):
"""A trading strategy capable of self tuning, training, and evaluating with Tensorforce."""
def __init__(self,
environment: TradingEnvironment,
agent_spec: Dict = None,
network_spec: Dict = None,
**kwargs):
"""
Arguments:
environment: A `TradingEnvironment` instance for the agent to trade within.
agent_spec: A specification dictionary for the `Tensorforce` agent.
network_sepc: A specification dictionary for the `Tensorforce` agent's model network.
kwargs (optional): Optional keyword arguments to adjust the strategy.
"""
self._environment = environment
self._max_episode_timesteps = kwargs.get('max_episode_timesteps', None)
if agent_spec and network_spec:
self._agent_spec = agent_spec
self._network_spec = network_spec
self._agent = Agent.from_spec(spec=agent_spec,
kwargs=dict(
network=network_spec,
states=environment.states,
actions=environment.actions))
self._runner = Runner(agent=self._agent, environment=environment)
@property
def agent(self) -> Agent:
"""A Tensorforce `Agent` instance that will learn the strategy."""
return self._agent
@property
def max_episode_timesteps(self) -> int:
"""The maximum timesteps per episode."""
return self._max_episode_timesteps
@max_episode_timesteps.setter
def max_episode_timesteps(self, max_episode_timesteps: int):
self._max_episode_timesteps = max_episode_timesteps
def restore_agent(self, path: str, model_path: str = None):
"""Deserialize the strategy's learning agent from a file.
Arguments:
path: The `str` path of the file the agent specification is stored in.
The `.json` file extension will be automatically appended if not provided.
model_path (optional): The `str` path of the file or directory the agent checkpoint is stored in.
If not provided, the `model_path` will default to `{path_without_dot_json}/agents`.
"""
path_with_ext = path if path.endswith('.json') else f'{path}.json'
with open(path_with_ext) as json_file:
spec = json.load(json_file)
self._agent_spec = spec.agent
self._network_spec = spec.network
self._agent = Agent.from_spec(spec=self._agent_spec,
kwargs=dict(
network=self._network_spec,
states=self._environment.states,
actions=self._environment.actions))
path_without_ext = path_with_ext.replace('.json', '')
model_path = model_path or f'{path_without_ext}/agent'
self._agent.restore_model(file=model_path)
self._runner = Runner(agent=self._agent, environment=self._environment)
def save_agent(self,
path: str,
model_path: str = None,
append_timestep: bool = False):
"""Serialize the learning agent to a file for restoring later.
Arguments:
path: The `str` path of the file to store the agent specification in.
The `.json` file extension will be automatically appended if not provided.
model_path (optional): The `str` path of the directory to store the agent checkpoints in.
If not provided, the `model_path` will default to `{path_without_dot_json}/agents`.
append_timestep: Whether the timestep should be appended to filename to prevent overwriting previous models.
Defaults to `False`.
"""
path_with_ext = path if path.endswith('.json') else f'{path}.json'
spec = {'agent': self._agent_spec, 'network': self._network_spec}
with open(path_with_ext, 'w') as json_file:
json.dump(spec, json_file)
path_without_ext = path_with_ext.replace('.json', '')
model_path = model_path or f'{path_without_ext}/agent'
if not os.path.exists(model_path):
os.makedirs(model_path)
self._agent.save_model(directory=model_path, append_timestep=True)
def _finished_episode_cb(self, runner: Runner) -> bool:
n_episodes = runner.episode
n_timesteps = runner.episode_timestep
avg_reward = np.mean(runner.episode_rewards)
print(f"Finished episode {n_episodes} after {n_timesteps} timesteps.")
print(f"Average episode reward: {avg_reward})")
return True
def tune(self,
steps: int = None,
episodes: int = None,
callback: Callable[[pd.DataFrame], bool] = None) -> pd.DataFrame:
raise NotImplementedError
def run(
self,
steps: int = None,
episodes: int = None,
should_train: bool = False,
episode_callback: Callable[[pd.DataFrame],
bool] = None) -> pd.DataFrame:
testing = not should_train
self._runner.run(testing=testing,
num_timesteps=steps,
num_episodes=episodes,
max_episode_timesteps=self._max_episode_timesteps,
episode_finished=self._finished_episode_cb)
self._runner.close()
n_episodes = self._runner.episode
n_timesteps = self._runner.timestep
avg_reward = np.mean(self._runner.episode_rewards)
print("Finished running strategy.")
print(f"Total episodes: {n_episodes} ({n_timesteps} timesteps).")
print(f"Average reward: {avg_reward}.")
def main():
parser = argparse.ArgumentParser(description='Tensorforce runner')
parser.add_argument(
'agent', help='Agent (configuration JSON file, name, or library module)'
)
parser.add_argument(
'environment',
help='Environment (name, configuration JSON file, or library module)'
)
# Agent arguments
parser.add_argument(
'-n', '--network', type=str, default=None,
help='Network (configuration JSON file, name, or library module)'
)
# Environment arguments
parser.add_argument(
'-l', '--level', type=str, default=None,
help='Level or game id, like `CartPole-v1`, if supported'
)
parser.add_argument(
'--visualize', action='store_true',
help='Visualize agent--environment interaction, if supported'
)
parser.add_argument(
'-i', '--import-modules', type=str, default=None,
help='Import comma-separated modules required for environment'
)
# Runner arguments
parser.add_argument('-t', '--timesteps', type=int, default=None, help='Number of timesteps')
parser.add_argument('-e', '--episodes', type=int, default=None, help='Number of episodes')
parser.add_argument(
'-m', '--max-episode-timesteps', type=int, default=None,
help='Maximum number of timesteps per episode'
),
parser.add_argument(
'--mean-horizon', type=int, default=10,
help='Number of timesteps/episodes for mean reward computation'
)
parser.add_argument('-v', '--evaluation', action='store_true', help='Evaluation mode')
parser.add_argument(
'-s', '--save-best-agent', action='store_true', help='Save best-performing agent'
)
# Logging arguments
parser.add_argument('-r', '--repeat', type=int, default=1, help='Number of repetitions')
parser.add_argument(
'-p', '--path', type=str, default=None,
help='Logging path, directory plus filename without extension'
)
parser.add_argument('--seaborn', action='store_true', help='Use seaborn')
args = parser.parse_args()
if args.import_modules is not None:
for module in args.import_modules.split(','):
importlib.import_module(name=module)
if args.path is None:
callback = None
else:
assert os.path.splitext(args.path)[1] == ''
assert args.episodes is not None and args.visualize is not None
rewards = [list() for _ in range(args.episodes)]
timesteps = [list() for _ in range(args.episodes)]
seconds = [list() for _ in range(args.episodes)]
agent_seconds = [list() for _ in range(args.episodes)]
def callback(r):
rewards[r.episode - 1].append(r.episode_reward)
timesteps[r.episode - 1].append(r.episode_timestep)
seconds[r.episode - 1].append(r.episode_second)
agent_seconds[r.episode - 1].append(r.episode_agent_second)
return True
if args.visualize:
if args.level is None:
environment = Environment.create(environment=args.environment, visualize=True)
else:
environment = Environment.create(
environment=args.environment, level=args.level, visualize=True
)
else:
if args.level is None:
environment = Environment.create(environment=args.environment)
else:
environment = Environment.create(environment=args.environment, level=args.level)
for _ in range(args.repeat):
agent_kwargs = dict()
if args.network is not None:
agent_kwargs['network'] = args.network
if args.max_episode_timesteps is not None:
assert environment.max_episode_timesteps() is None or \
environment.max_episode_timesteps() == args.max_episode_timesteps
agent_kwargs['max_episode_timesteps'] = args.max_episode_timesteps
agent = Agent.create(agent=args.agent, environment=environment, **agent_kwargs)
runner = Runner(agent=agent, environment=environment)
runner.run(
num_timesteps=args.timesteps, num_episodes=args.episodes,
max_episode_timesteps=args.max_episode_timesteps, callback=callback,
mean_horizon=args.mean_horizon, evaluation=args.evaluation
# save_best_model=args.save_best_model
)
runner.close()
if args.path is not None:
if not os.path.isdir(os.path.split(args.path)[0]):
os.makedirs(os.path.split(args.path)[0], exist_ok=True)
with open(args.path + '.json', 'w') as filehandle:
filehandle.write(
json.dumps(dict(
rewards=rewards, timesteps=timesteps, seconds=seconds,
agent_seconds=agent_seconds
))
)
if args.seaborn:
import seaborn as sns
sns.set()
xs = np.arange(len(rewards))
min_rewards = np.amin(rewards, axis=1)
max_rewards = np.amax(rewards, axis=1)
median_rewards = np.median(rewards, axis=1)
plt.plot(xs, median_rewards, color='green', linewidth=2.0)
plt.fill_between(xs, min_rewards, max_rewards, color='green', alpha=0.4)
plt.xlabel('episodes')
plt.ylabel('reward')
plt.savefig(fname=(args.path + '.png'))
class TensorforceTradingStrategy(TradingStrategy):
"""A trading strategy capable of self tuning, training, and evaluating with Tensorforce."""
def __init__(self,
environment: 'TradingEnvironment',
agent_spec: any,
save_best_agent: bool = False,
**kwargs):
"""
Arguments:
environment: A `TradingEnvironment` instance for the agent to trade within.
agent: A `Tensorforce` agent or agent specification.
save_best_agent (optional): The runner will automatically save the best agent
kwargs (optional): Optional keyword arguments to adjust the strategy.
"""
self._max_episode_timesteps = kwargs.get('max_episode_timesteps',
False)
self._environment = Environment.create(
environment='gym',
level=environment,
max_episode_timesteps=self._max_episode_timesteps)
self._agent = Agent.create(agent=agent_spec,
environment=self._environment)
self._runner = Runner(agent=self._agent,
environment=self._environment,
save_best_agent=save_best_agent)
@property
def agent(self) -> Agent:
"""A Tensorforce `Agent` instance that will learn the strategy."""
return self._agent
@property
def max_episode_timesteps(self) -> int:
"""The maximum timesteps per episode."""
return self._max_episode_timesteps
@max_episode_timesteps.setter
def max_episode_timesteps(self, max_episode_timesteps: int):
self._max_episode_timesteps = max_episode_timesteps
def restore_agent(self, directory: str, filename: str = None):
"""Deserialize the strategy's learning agent from a file.
Arguments:
directory: The `str` path of the directory the agent checkpoint is stored in.
filename (optional): The `str` path of the file the agent specification is stored in.
The `.json` file extension will be automatically appended if not provided.
"""
self._agent = Agent.load(directory, filename=filename)
self._runner = Runner(agent=self._agent, environment=self._environment)
def save_agent(self,
directory: str,
filename: str = None,
append_timestep: bool = False):
"""Serialize the learning agent to a file for restoring later.
Arguments:
directory: The `str` path of the directory the agent checkpoint is stored in.
filename (optional): The `str` path of the file the agent specification is stored in.
The `.json` file extension will be automatically appended if not provided.
append_timestep: Whether the timestep should be appended to filename to prevent overwriting previous models.
Defaults to `False`.
"""
self._agent.save(directory=directory,
filename=filename,
append_timestep=append_timestep)
def _finished_episode_cb(self, runner: Runner) -> bool:
n_episodes = runner.episodes
n_timesteps = runner.episode_timesteps
avg_reward = np.mean(runner.episode_rewards)
print("Finished episode {} after {} timesteps.".format(
n_episodes, n_timesteps))
print("Average episode reward: {})".format(avg_reward))
return True
def tune(self,
steps: int = None,
episodes: int = None,
callback: Callable[[pd.DataFrame], bool] = None) -> pd.DataFrame:
raise NotImplementedError
def run(
self,
steps: int = None,
episodes: int = None,
evaluation: bool = False,
episode_callback: Callable[[pd.DataFrame],
bool] = None) -> pd.DataFrame:
self._runner.run(evaluation=evaluation,
num_timesteps=steps,
num_episodes=episodes,
callback=episode_callback)
n_episodes = self._runner.episodes
n_timesteps = self._runner.timesteps
avg_reward = np.mean(self._runner.episode_rewards)
print("Finished running strategy.")
print("Total episodes: {} ({} timesteps).".format(
n_episodes, n_timesteps))
print("Average reward: {}.".format(avg_reward))
self._runner.close()
return self._environment.environment._exchange._performance
def main(max_timesteps):
max_episodes = None
#max_timesteps = 86400000000*days
env = real_adapter(pong)
network_spec = [
#dict(type='flatten'),
dict(type='dense', size=11, activation='tanh'),
#dict(type='dense', size=20, activation='tanh'),
#dict(type='dense', size=32, activation='tanh'),
]
exploration = dict(type='epsilon_decay', timesteps=max_timesteps)
summarizer = dict(
directory="./models/"+str(datetime.now()).replace(' ', ''),
steps=10000,
seconds=None,
labels=[
#'rewards',
#'actions',
'inputs',
'gradients',
'configuration',
],
meta_dict=dict(
description='July 2: Trying 11 node hidden layer.',
layers=str(network_spec),
timesteps=max_timesteps,
exploration=exploration,
),
)
agent = NAFAgent(
states=env.states,
actions=env.actions,
network=network_spec,
#actions_exploration=exploration,
#summarizer=summarizer,
#batch_size=64
)
runner = Runner(agent, env)
report_episodes = 1
#global prev
global prev
prev = 0
def episode_finished(r):
global prev
if r.episode % report_episodes == 0:
#print("Finished episode {ep} after {ts} timesteps".format(ep=r.episode, ts=r.timestep-prev))
#print("Episode reward: {}".format(r.episode_rewards[-1]))
print(r.episode_rewards[-1])
prev = r.timestep
#print("Average of last 100 rewards: {}".format(sum(r.episode_rewards[-100:]) / 100))
return True
print("Starting {agent} for Environment '{env}'".format(agent=agent, env=env))
runner.run(num_episodes=max_episodes, num_timesteps=max_timesteps, max_episode_timesteps=None, episode_finished=episode_finished)
agent.save_model(directory='./results/NAF/'+str(datetime.now()).replace(' ', '')+'/model')
runner.close()
print("Learning finished. Total episodes: {ep}".format(ep=runner.episode))
class UnittestBase(object):
"""
Unit-test base class.
"""
# Unittest
num_updates = None
num_episodes = None
num_timesteps = None
# Environment
timestep_range = (1, 5)
states = dict(bool_state=dict(type='bool', shape=(1, )),
int_state=dict(type='int', shape=(2, ), num_values=4),
float_state=dict(type='float', shape=(1, 1, 2)),
bounded_state=dict(type='float',
shape=(),
min_value=-0.5,
max_value=0.5))
actions = dict(bool_action=dict(type='bool', shape=(1, )),
int_action=dict(type='int', shape=(2, ), num_values=4),
float_action=dict(type='float', shape=(1, 1)),
bounded_action=dict(type='float',
shape=(2, ),
min_value=-0.5,
max_value=0.5))
# Exclude action types
exclude_bool_action = False
exclude_int_action = False
exclude_float_action = False
exclude_bounded_action = False
# Agent
agent = dict(update=4,
network=dict(type='auto', size=8, internal_rnn=2),
objective='policy_gradient',
reward_estimation=dict(horizon=2))
# Tensorforce config
require_observe = False
require_all = False
def start_tests(self, name=None):
"""
Start unit-test method.
"""
if name is None:
sys.stdout.write('\n{} {}: '.format(
datetime.now().strftime('%H:%M:%S'),
self.__class__.__name__[4:]))
else:
sys.stdout.write('\n{} {} ({}): '.format(
datetime.now().strftime('%H:%M:%S'),
self.__class__.__name__[4:], name))
sys.stdout.flush()
def finished_test(self, assertion=None):
"""
Finished unit-test.
"""
if assertion is None:
assertion = True
else:
self.assertTrue(expr=assertion)
if assertion:
sys.stdout.write('.')
sys.stdout.flush()
def prepare(self,
environment=None,
timestep_range=None,
states=None,
actions=None,
exclude_bool_action=False,
exclude_int_action=False,
exclude_float_action=False,
exclude_bounded_action=False,
require_observe=False,
require_all=False,
**agent):
"""
Generic unit-test preparation.
"""
Layer.layers = None
if environment is None:
if states is None:
states = deepcopy(self.__class__.states)
if actions is None:
actions = deepcopy(self.__class__.actions)
if exclude_bool_action or self.__class__.exclude_bool_action:
actions.pop('bool_action')
if exclude_int_action or self.__class__.exclude_int_action:
actions.pop('int_action')
if exclude_float_action or self.__class__.exclude_float_action:
actions.pop('float_action')
if exclude_bounded_action or self.__class__.exclude_bounded_action:
actions.pop('bounded_action')
if timestep_range is None:
timestep_range = self.__class__.timestep_range
environment = UnittestEnvironment(
states=states,
actions=actions,
timestep_range=timestep_range,
)
elif timestep_range is not None:
raise TensorforceError.unexpected()
environment = Environment.create(environment=environment)
for key, value in self.__class__.agent.items():
if key not in agent:
agent[key] = value
if self.__class__.require_all or require_all:
config = None
elif self.__class__.require_observe or require_observe:
config = dict(api_functions=['reset', 'act', 'observe'])
else:
config = dict(api_functions=['reset', 'act'])
agent = Agent.create(agent=agent,
environment=environment,
config=config)
return agent, environment
def unittest(self,
num_updates=None,
num_episodes=None,
num_timesteps=None,
environment=None,
timestep_range=None,
states=None,
actions=None,
exclude_bool_action=False,
exclude_int_action=False,
exclude_float_action=False,
exclude_bounded_action=False,
require_observe=False,
require_all=False,
**agent):
"""
Generic unit-test.
"""
agent, environment = self.prepare(
environment=environment,
timestep_range=timestep_range,
states=states,
actions=actions,
exclude_bool_action=exclude_bool_action,
exclude_int_action=exclude_int_action,
exclude_float_action=exclude_float_action,
exclude_bounded_action=exclude_bounded_action,
require_observe=require_observe,
require_all=require_all,
**agent)
self.runner = Runner(agent=agent, environment=environment)
assert (num_updates is not None) + (num_episodes is not None) + \
(num_timesteps is not None) <= 1
if num_updates is None and num_episodes is None and num_timesteps is None:
num_updates = self.__class__.num_updates
num_episodes = self.__class__.num_episodes
num_timesteps = self.__class__.num_timesteps
if num_updates is None and num_episodes is None and num_timesteps is None:
num_updates = 2
assert (num_updates is not None) + (num_episodes is not None) + \
(num_timesteps is not None) == 1
evaluation = not any([
require_all, require_observe, self.__class__.require_all,
self.__class__.require_observe
])
self.runner.run(num_episodes=num_episodes,
num_timesteps=num_timesteps,
num_updates=num_updates,
max_episode_timesteps=agent.max_episode_timesteps,
use_tqdm=False,
evaluation=evaluation)
self.runner.close()
self.finished_test()
def main(args):
version = 'v1'
episodes = args.episodes
visualize = args.visualize
config = ffa_v0_fast_env()
env = Pomme(**config["env_kwargs"])
env.seed(0)
agent = PPOAgent(
states=dict(type='float', shape=(11, 11, 12)),
actions=dict(type='int', num_actions=env.action_space.n),
network=[
# (9, 9, 12)
dict(type='conv2d', size=12, window=3, stride=1),
# (7, 7, 8)
dict(type='conv2d', size=8, window=3, stride=1),
# (5, 5, 4)
dict(type='conv2d', size=4, window=3, stride=1),
# (100)
dict(type='flatten'),
dict(type='dense', size=64, activation='relu'),
dict(type='dense', size=16, activation='relu'),
],
batching_capacity=1000,
step_optimizer=dict(type='adam', learning_rate=1e-4))
if os.path.exists(os.path.join('models', version, 'checkpoint')):
agent.restore_model(directory=os.path.join('models', version))
agents = []
for agent_id in range(3):
# agents.append(RandomAgent(config["agent"](agent_id, config["game_type"])))
# agents.append(StoppingAgent(config["agent"](agent_id, config["game_type"])))
agents.append(
SimpleAgent(config["agent"](agent_id, config["game_type"])))
agent_id += 1
agents.append(
TensorforceAgent(config["agent"](agent_id, config["game_type"])))
env.set_agents(agents)
env.set_training_agent(agents[-1].agent_id)
env.set_init_game_state(None)
wrapped_env = WrappedEnv(env, agent, visualize)
runner = Runner(agent=agent, environment=wrapped_env)
try:
runner.run(episodes=episodes, max_episode_timesteps=100)
except Exception as e:
raise e
finally:
agent.save_model(directory=os.path.join('models', version, 'agent'))
win_count = len(
list(filter(lambda reward: reward == 1, runner.episode_rewards)))
print('Stats: ')
print(f' runner.episode_rewards = {runner.episode_rewards}')
print(f' win count = {win_count}')
try:
runner.close()
except AttributeError as e:
raise e
def main():
parser = argparse.ArgumentParser()
parser.add_argument('gym_id', help="Id of the Gym environment")
parser.add_argument('-a', '--agent', help="Agent configuration file")
parser.add_argument('-n',
'--network',
default=None,
help="Network specification file")
parser.add_argument('-e',
'--episodes',
type=int,
default=None,
help="Number of episodes")
parser.add_argument('-t',
'--timesteps',
type=int,
default=None,
help="Number of timesteps")
parser.add_argument('-m',
'--max-episode-timesteps',
type=int,
default=None,
help="Maximum number of timesteps per episode")
parser.add_argument('-d',
'--deterministic',
action='store_true',
default=False,
help="Choose actions deterministically")
parser.add_argument('-l', '--load', help="Load agent from this dir")
parser.add_argument('--monitor', help="Save results to this directory")
parser.add_argument('--monitor-safe',
action='store_true',
default=False,
help="Do not overwrite previous results")
parser.add_argument('--monitor-video',
type=int,
default=0,
help="Save video every x steps (0 = disabled)")
parser.add_argument('-D',
'--debug',
action='store_true',
default=False,
help="Show debug outputs")
args = parser.parse_args()
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__file__)
logger.setLevel(logging.INFO)
environment = OpenAIGym(gym_id=args.gym_id,
monitor=args.monitor,
monitor_safe=args.monitor_safe,
monitor_video=args.monitor_video)
if args.agent is not None:
with open(args.agent, 'r') as fp:
agent = json.load(fp=fp)
else:
raise TensorForceError("No agent configuration provided.")
if args.network is not None:
with open(args.network, 'r') as fp:
network = json.load(fp=fp)
else:
network = None
logger.info("No network configuration provided.")
agent = Agent.from_spec(spec=agent,
kwargs=dict(states=environment.states,
actions=environment.actions,
network=network))
if args.load:
load_dir = os.path.dirname(args.load)
if not os.path.isdir(load_dir):
raise OSError(
"Could not load agent from {}: No such directory.".format(
load_dir))
agent.restore_model(args.load)
if args.debug:
logger.info("-" * 16)
logger.info("Configuration:")
logger.info(agent)
runner = Runner(agent=agent, environment=environment, repeat_actions=1)
if args.debug: # TODO: Timestep-based reporting
report_episodes = 1
else:
report_episodes = 100
logger.info("Starting {agent} for Environment '{env}'".format(
agent=agent, env=environment))
def episode_finished(r):
if r.episode % report_episodes == 0:
steps_per_second = r.timestep / (time.time() - r.start_time)
logger.info(
"Finished episode {:d} after {:d} timesteps. Steps Per Second {:0.2f}"
.format(r.agent.episode, r.episode_timestep, steps_per_second))
logger.info("Episode reward: {}".format(r.episode_rewards[-1]))
logger.info("Average of last 500 rewards: {:0.2f}".format(
sum(r.episode_rewards[-500:]) /
min(500, len(r.episode_rewards))))
logger.info("Average of last 100 rewards: {:0.2f}".format(
sum(r.episode_rewards[-100:]) /
min(100, len(r.episode_rewards))))
return True
runner.run(timesteps=args.timesteps,
episodes=args.episodes,
max_episode_timesteps=args.max_episode_timesteps,
deterministic=args.deterministic,
episode_finished=episode_finished)
runner.close()
logger.info("Learning finished. Total episodes: {ep}".format(
ep=runner.agent.episode))
def main():
parser = argparse.ArgumentParser(description="Playground Flags.")
parser.add_argument("--game", default="pommerman", help="Game to choose.")
parser.add_argument("--config",
default="PommeFFA-v0",
help="Configuration to execute. See env_ids in "
"configs.py for options.")
parser.add_argument("--agents",
default="tensorforce::ppo,test::agents.SimpleAgent,"
"test::agents.SimpleAgent,test::agents.SimpleAgent",
help="Comma delineated list of agent types and docker "
"locations to run the agents.")
parser.add_argument("--agent_env_vars",
help="Comma delineated list of agent environment vars "
"to pass to Docker. This is only for the Docker Agent."
" An example is '0:foo=bar:baz=lar,3:foo=lam', which "
"would send two arguments to Docker Agent 0 and one to"
" Docker Agent 3.",
default="")
parser.add_argument("--record_pngs_dir",
default=None,
help="Directory to record the PNGs of the game. "
"Doesn't record if None.")
parser.add_argument("--record_json_dir",
default=None,
help="Directory to record the JSON representations of "
"the game. Doesn't record if None.")
parser.add_argument("--render",
default=True,
help="Whether to render or not. Defaults to True.")
parser.add_argument("--game_state_file",
default=None,
help="File from which to load game state. Defaults to "
"None.")
args = parser.parse_args()
config = args.config
record_pngs_dir = args.record_pngs_dir
record_json_dir = args.record_json_dir
agent_env_vars = args.agent_env_vars
game_state_file = args.game_state_file
# TODO: After https://github.com/MultiAgentLearning/playground/pull/40
# this is still missing the docker_env_dict parsing for the agents.
agents = [
helpers.make_agent_from_string(agent_string, agent_id + 1000)
for agent_id, agent_string in enumerate(args.agents.split(","))
]
env = make(config, agents, game_state_file)
training_agent = None
for agent in agents:
if type(agent) == TensorForceAgent:
training_agent = agent
env.set_training_agent(agent.agent_id)
break
if args.record_pngs_dir:
assert not os.path.isdir(args.record_pngs_dir)
os.makedirs(args.record_pngs_dir)
if args.record_json_dir:
assert not os.path.isdir(args.record_json_dir)
os.makedirs(args.record_json_dir)
# Create a Proximal Policy Optimization agent
agent = training_agent.initialize(env)
atexit.register(functools.partial(clean_up_agents, agents))
wrapped_env = WrappedEnv(env, visualize=args.render)
runner = Runner(agent=agent, environment=wrapped_env)
runner.run(episodes=10, max_episode_timesteps=2000)
print("Stats: ", runner.episode_rewards, runner.episode_timesteps,
runner.episode_times)
try:
runner.close()
except AttributeError as e:
pass
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--mode', help="ID of the game mode")
parser.add_argument('--hide',
dest='hide',
action='store_const',
const=True,
default=False,
help="Hide output window")
parser.add_argument('-a',
'--agent-config',
help="Agent configuration file")
parser.add_argument('-n',
'--network-spec',
default=None,
help="Network specification file")
parser.add_argument('-e',
'--episodes',
type=int,
default=50000,
help="Number of episodes")
parser.add_argument('-t',
'--max-timesteps',
type=int,
default=2000,
help="Maximum number of timesteps per episode")
parser.add_argument('-s', '--save', help="Save agent to this dir")
parser.add_argument('-se',
'--save-episodes',
type=int,
default=100,
help="Save agent every x episodes")
parser.add_argument('-l', '--load', help="Load agent from this dir")
parser.add_argument('-D',
'--debug',
action='store_true',
default=False,
help="Show debug outputs")
args = parser.parse_args()
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG) # configurable!!!
environment = MazeExplorer(mode_id=args.mode, visible=not args.hide)
if args.agent_config is not None:
with open(args.agent_config, 'r') as fp:
agent_config = json.load(fp=fp)
else:
raise TensorForceError("No agent configuration provided.")
if args.network_spec is not None:
with open(args.network_spec, 'r') as fp:
network = json.load(fp=fp)
else:
network = None
logger.info("No network configuration provided.")
agent = Agent.from_spec(spec=agent_config,
kwargs=dict(states=environment.states,
actions=environment.actions,
network=network))
if args.load:
load_dir = os.path.dirname(args.load)
if not os.path.isdir(load_dir):
raise OSError(
"Could not load agent from {}: No such directory.".format(
load_dir))
agent.restore_model(args.load)
if args.debug:
logger.info("-" * 16)
logger.info("Configuration:")
logger.info(agent_config)
if args.save:
save_dir = os.path.dirname(args.save)
if not os.path.isdir(save_dir):
try:
os.mkdir(save_dir, 0o755)
except OSError:
raise OSError(
"Cannot save agent to dir {} ()".format(save_dir))
runner = Runner(agent=agent, environment=environment, repeat_actions=1)
report_episodes = args.episodes // 1000
if args.debug:
report_episodes = 1
def episode_finished(r):
if r.episode % report_episodes == 0:
sps = r.timestep / (time.time() - r.start_time)
logger.info(
"Finished episode {ep} after {ts} timesteps. Steps Per Second {sps}"
.format(ep=r.episode, ts=r.timestep, sps=sps))
logger.info("Episode reward: {}".format(r.episode_rewards[-1]))
logger.info("Average of last 500 rewards: {}".format(
sum(r.episode_rewards[-500:]) / 500))
logger.info("Average of last 100 rewards: {}".format(
sum(r.episode_rewards[-100:]) / 100))
return True
logger.info("Starting {agent} for Environment '{env}'".format(
agent=agent, env=environment))
runner.run(args.episodes,
args.max_timesteps,
episode_finished=episode_finished)
runner.close()
logger.info(
"Learning finished. Total episodes: {ep}".format(ep=runner.episode))
environment.close()
def main(argv):
logging_basicConfig(level=INFO)
logger = getLogger(__file__)
logger.setLevel(INFO)
environment = OpenAIGym(
gym_id='MoveToBeacon-bbueno5000-v0',
monitor=FLAGS.monitor,
monitor_safe=FLAGS.monitor_safe,
monitor_video=FLAGS.monitor_video,
visualize=FLAGS.visualize)
# if FLAGS.agent_config is not None:
# with open(FLAGS.agent_config, 'r') as fp:
# agent_config = json.load(fp=fp)
# else:
# raise TensorForceError(
# "No agent configuration provided.")
# if FLAGS.network is not None:
# with open(FLAGS.network, 'r') as fp:
# network = json.load(fp=fp)
# else:
# network = None
# logger.info(
# "No network configuration provided.")
network_spec = [
dict(type='flatten'),
dict(type='dense', size=32),
dict(type='dense', size=32)
]
agent = PPOAgent(
states=environment.states,
actions=environment.actions,
network=network_spec
)
if FLAGS.load:
load_dir = path.dirname(FLAGS.load)
if not path.isdir(load_dir):
raise OSError(
"Could not load agent from {}: No such directory.".format(load_dir))
agent.restore_model(FLAGS.load)
if FLAGS.save:
save_dir = path.dirname(FLAGS.save)
if not path.isdir(save_dir):
try:
mkdir(save_dir, 0o755)
except OSError:
raise OSError(
"Cannot save agent to dir {} ()".format(save_dir))
if FLAGS.debug:
logger.info("-" * 16)
logger.info("Configuration:")
logger.info(agent)
runner = Runner(
agent=agent,
environment=environment,
repeat_actions=1)
if FLAGS.debug:
report_episodes = 1
else:
report_episodes = 100
logger.info(
"Starting {agent} for Environment {env}".format(
agent=agent, env=environment))
def episode_finished(r, id_):
if r.episode % report_episodes == 0:
steps_per_second = r.timestep / (time() - r.start_time)
logger.info("Finished episode {:d} after {:d} timesteps. Steps Per Second {:0.2f}".format(
r.agent.episode, r.episode_timestep, steps_per_second))
logger.info("Episode reward: {}".format(r.episode_rewards[-1]))
logger.info("Average of last 500 rewards: {:0.2f}".format(
sum(r.episode_rewards[-500:]) / min(500, len(r.episode_rewards))))
logger.info("Average of last 100 rewards: {:0.2f}".format(
sum(r.episode_rewards[-100:]) / min(100, len(r.episode_rewards))))
if FLAGS.save and FLAGS.save_episodes is not None and not r.episode % FLAGS.save_episodes:
logger.info("Saving agent to {}".format(FLAGS.save))
r.agent.save_model(FLAGS.save)
return True
runner.run(
num_timesteps=FLAGS.timesteps,
num_episodes=FLAGS.num_episodes,
max_episode_timesteps=FLAGS.max_episode_timesteps,
deterministic=FLAGS.deterministic,
episode_finished=episode_finished,
testing=FLAGS.test,
sleep=FLAGS.sleep)
runner.close()
logger.info("Learning completed.")
logger.info("Total episodes: {ep}".format(ep=runner.agent.episode))
def main():
'''CLI interface to bootstrap taining'''
parser = argparse.ArgumentParser(description="Playground Flags.")
parser.add_argument("--game", default="pommerman", help="Game to choose.")
parser.add_argument("--config",
default="PommeFFACompetition-v0",
help="Configuration to execute. See env_ids in "
"configs.py for options.")
parser.add_argument("--agents",
default="tensorforce::ppo,test::agents.SimpleAgent,"
"test::agents.SimpleAgent,test::agents.SimpleAgent",
help="Comma delineated list of agent types and docker "
"locations to run the agents.")
parser.add_argument("--agent_env_vars",
help="Comma delineated list of agent environment vars "
"to pass to Docker. This is only for the Docker Agent."
" An example is '0:foo=bar:baz=lar,3:foo=lam', which "
"would send two arguments to Docker Agent 0 and one to"
" Docker Agent 3.",
default="")
parser.add_argument("--record_pngs_dir",
default=None,
help="Directory to record the PNGs of the game. "
"Doesn't record if None.")
parser.add_argument("--record_json_dir",
default=None,
help="Directory to record the JSON representations of "
"the game. Doesn't record if None.")
parser.add_argument("--render",
default=False,
action='store_true',
help="Whether to render or not. Defaults to False.")
parser.add_argument("--game_state_file",
default=None,
help="File from which to load game state. Defaults to "
"None.")
parser.add_argument("--checkpoint",
default="models/ppo",
help="Directory where checkpoint file stored to.")
parser.add_argument("--num_of_episodes",
default="10",
help="Number of episodes")
parser.add_argument("--max_timesteps",
default="2000",
help="Number of steps")
args = parser.parse_args()
config = args.config
record_pngs_dir = args.record_pngs_dir
record_json_dir = args.record_json_dir
agent_env_vars = args.agent_env_vars
game_state_file = args.game_state_file
checkpoint = args.checkpoint
num_of_episodes = int(args.num_of_episodes)
max_timesteps = int(args.max_timesteps)
# TODO: After https://github.com/MultiAgentLearning/playground/pull/40
# this is still missing the docker_env_dict parsing for the agents.
agents = [
helpers.make_agent_from_string(agent_string, agent_id + 1000)
for agent_id, agent_string in enumerate(args.agents.split(","))
]
env = make(config, agents, game_state_file)
training_agent = None
for agent in agents:
if type(agent) == TensorForceAgent:
training_agent = agent
env.set_training_agent(agent.agent_id)
break
if args.record_pngs_dir:
assert not os.path.isdir(args.record_pngs_dir)
os.makedirs(args.record_pngs_dir)
if args.record_json_dir:
assert not os.path.isdir(args.record_json_dir)
os.makedirs(args.record_json_dir)
# Create a Proximal Policy Optimization agent
agent = training_agent.initialize(env)
atexit.register(functools.partial(clean_up_agents, agents))
wrapped_env = WrappedEnv(env, visualize=args.render)
runner = Runner(agent=agent, environment=wrapped_env)
runner.run(episodes=num_of_episodes, max_episode_timesteps=max_timesteps)
print("Stats: ", runner.episode_rewards[-30:], runner.episode_timesteps,
runner.episode_times)
agent.save_model(checkpoint)
rewards = runner.episode_rewards
win = rewards.count(1)
lose = rewards.count(-1)
draw = rewards.count(0)
total = win + lose + draw
ratio = round((win / total) * 100.0, 2)
print("Results ({}%) = Win({}), Lose({}), Draw({})".format(
ratio, win, lose, draw))
try:
runner.close()
except AttributeError as e:
pass
def main():
# SET BASIC PARAMETERS
start_time = time.time()
random_seed = 21
agent_save_period = 500
visualize_period = 1
run_number = 965
load_agent = False
agent_filename = '371-P33-27-PPO-2000'
to_visualize = False
# Set logging level
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger()
logger.setLevel(logging.INFO)
# if args.import_modules is not None:
# for module in args.import_modules.split(','):
# importlib.import_module(name=module)
environment = Environment.create(environment='gym',
level='EnvTestContinuousR-v2',
visualize=to_visualize)
# Set random seed for environment
environment.environment.env.seed(random_seed)
environment.environment.env.set_reward(3)
environment.environment.env.set_random(3)
environment.environment.env.set_reward_scale(6)
# Initialize Agent-Network-Model objects
with open(
'C:\\Users\\genia\\Source\\Repos\\Box2dEnv\\examples\\configs\\ppo-new3.json',
'r') as fp:
agentSpec = json.load(fp=fp)
with open(
'C:\\Users\\genia\\Source\\Repos\\Box2dEnv\\examples\\configs\\mlp2_network-new.json',
'r') as fp:
network = json.load(fp=fp)
# agentSpec['update_mode'].update(batch_size=24)
# agentSpec['update_mode'].update(frequency=24)
#agentSpec['baseline']['sizes'] = [512,512]
agentSpec['optimization_steps'] = 9
agentSpec['network']['layers'][0]['size'] = 128
agentSpec['network']['layers'][1]['size'] = 129
agentSpec['critic_network']['layers'][0]['size'] = 126
agentSpec['critic_network']['layers'][1]['size'] = 127
agentSpec['batch_size'] = 13
agentSpec['subsampling_fraction'] = 0.8
agentSpec['critic_optimizer']['num_steps'] = 11
agentSpec['likelihood_ratio_clipping'] = 0.2
# network[0].update(size=512)
# network[1].update(size=512)
# agentSpec['network']['layers'] = network
# agentSpec['critic_network']['layers'] = network
agent = Agent.create(
max_episode_timesteps=3000,
agent=agentSpec,
environment=environment,
seed=random_seed
# kwargs=dict(
# states=environment.states,
# actions=environment.actions,
# network=network,
# #random_seed=random_seed
)
agent.initialize()
# print("Agent memory ", agent.memory['capacity'])
# print("Agent baseline steps", agent.baseline_optimizer['num_steps'])
# print("Agent optimizer steps", agent.optimizer['num_steps'])
if load_agent:
agent.restore_model(
directory=
'C:\\Users\\genia\\Source\\Repos\\Box2dEnv\\Box2dEnv\\saves\\modelSave',
file=agent_filename)
runner = Runner(agent=agent, environment=environment)
# logger.info("Starting {agent} for Environment '{env}'".format(agent=agent, env=environment))
# Naming variables
nNum = str(run_number).zfill(3)
task = environment.environment.env.task
if task == 'LIFT':
nTask = 'L'
else:
nTask = 'P'
nReward = environment.environment.env.reward_level
nRandom = environment.environment.env.rand_level
nSeed = str(random_seed).zfill(2)
nAlg = 'PPO'
nName = ("{}-{}{}{}-{}-{}".format(nNum, nTask, nReward, nRandom, nSeed,
nAlg))
def episode_finished(r, id_=None):
# if r.episode == 1:
# r.agent.restore_model('C:\\Users\\genia\\Source\\Repos\\Box2dEnv\\Box2dEnv\\saves\\modelSave')
save_period = 5
if r.episodes % visualize_period == 0:
if to_visualize:
environment.visualize = True # Set to true to visualize
else:
environment.visualize = False
if r.episodes % save_period == 0:
with open(
'C:\\Users\\genia\\Source\\Repos\\Box2dEnv\\Box2dEnv\\saves\\{}.csv'
.format(nName), 'a+') as csv:
for reward in r.episode_rewards[-save_period:]:
csv.write("{:2.2f}\n".format(reward))
# print("\nSaving, yo!")
if r.episodes == 1 or (r.episodes % agent_save_period == 0):
logger.info("\nSaving agent to {} at episode {}".format(
'C:\\Users\\genia\\Source\\Repos\\Box2dEnv\\Box2dEnv\\saves\\modelSave\\{}'
.format(nName), r.episodes))
# r.agent.save(
# directory='C:\\Users\\genia\\Source\\Repos\\Box2dEnv\\Box2dEnv\\saves\\modelSave\\{}{}'.format(nName, r.episodes),
# append_timestep=False)
return True
def episode_finish(r, id_=None):
print(r)
runner.run(
num_episodes=2000,
num_timesteps=10000000,
max_episode_timesteps=500,
num_repeat_actions=1,
# Callback
callback=episode_finished,
callback_episode_frequency=1,
callback_timestep_frequency=None,
# Tqdm
use_tqdm=True,
mean_horizon=100,
# Evaluation
evaluation=False,
evaluation_callback=None,
evaluation_frequency=None,
max_evaluation_timesteps=None,
num_evaluation_iterations=0)
runner.close()
logger.info("Learning finished. Total episodes: {ep}".format(
ep=runner.agent.episode))