def train_agent_with_evaluation(
agent,
env,
steps,
eval_n_steps,
eval_n_episodes,
eval_interval,
outdir,
checkpoint_freq=None,
train_max_episode_len=None,
step_offset=0,
eval_max_episode_len=None,
eval_env=None,
successful_score=None,
step_hooks=(),
save_best_so_far_agent=True,
use_tensorboard=False,
logger=None,
):
"""Train an agent while periodically evaluating it.
Args:
agent: A pfrl.agent.Agent
env: Environment train the agent against.
steps (int): Total number of timesteps for training.
eval_n_steps (int): Number of timesteps at each evaluation phase.
eval_n_episodes (int): Number of episodes at each evaluation phase.
eval_interval (int): Interval of evaluation.
outdir (str): Path to the directory to output data.
checkpoint_freq (int): frequency at which agents are stored.
train_max_episode_len (int): Maximum episode length during training.
step_offset (int): Time step from which training starts.
eval_max_episode_len (int or None): Maximum episode length of
evaluation runs. If None, train_max_episode_len is used instead.
eval_env: Environment used for evaluation.
successful_score (float): Finish training if the mean score is greater
than or equal to this value if not None
step_hooks (Sequence): Sequence of callable objects that accepts
(env, agent, step) as arguments. They are called every step.
See pfrl.experiments.hooks.
save_best_so_far_agent (bool): If set to True, after each evaluation
phase, if the score (= mean return of evaluation episodes) exceeds
the best-so-far score, the current agent is saved.
use_tensorboard (bool): Additionally log eval stats to tensorboard
logger (logging.Logger): Logger used in this function.
"""
logger = logger or logging.getLogger(__name__)
os.makedirs(outdir, exist_ok=True)
if eval_env is None:
eval_env = env
if eval_max_episode_len is None:
eval_max_episode_len = train_max_episode_len
evaluator = Evaluator(
agent=agent,
n_steps=eval_n_steps,
n_episodes=eval_n_episodes,
eval_interval=eval_interval,
outdir=outdir,
max_episode_len=eval_max_episode_len,
env=eval_env,
step_offset=step_offset,
save_best_so_far_agent=save_best_so_far_agent,
use_tensorboard=use_tensorboard,
logger=logger,
)
train_agent(
agent,
env,
steps,
outdir,
checkpoint_freq=checkpoint_freq,
max_episode_len=train_max_episode_len,
step_offset=step_offset,
evaluator=evaluator,
successful_score=successful_score,
step_hooks=step_hooks,
logger=logger,
)
def train_agent_batch_with_evaluation(
agent,
env,
steps,
eval_n_steps,
eval_n_episodes,
eval_interval,
outdir,
checkpoint_freq=None,
max_episode_len=None,
step_offset=0,
eval_max_episode_len=None,
return_window_size=100,
eval_env=None,
log_interval=None,
successful_score=None,
step_hooks=(),
evaluation_hooks=(),
save_best_so_far_agent=True,
use_tensorboard=False,
logger=None,
):
"""Train an agent while regularly evaluating it.
Args:
agent: Agent to train.
env: Environment train the againt against.
steps (int): Number of total time steps for training.
eval_n_steps (int): Number of timesteps at each evaluation phase.
eval_n_runs (int): Number of runs for each time of evaluation.
eval_interval (int): Interval of evaluation.
outdir (str): Path to the directory to output things.
log_interval (int): Interval of logging.
checkpoint_freq (int): frequency with which to store networks
max_episode_len (int): Maximum episode length.
step_offset (int): Time step from which training starts.
return_window_size (int): Number of training episodes used to estimate
the average returns of the current agent.
eval_max_episode_len (int or None): Maximum episode length of
evaluation runs. If set to None, max_episode_len is used instead.
eval_env: Environment used for evaluation.
successful_score (float): Finish training if the mean score is greater
or equal to thisvalue if not None
step_hooks (Sequence): Sequence of callable objects that accepts
(env, agent, step) as arguments. They are called every step.
See pfrl.experiments.hooks.
evaluation_hooks (Sequence): Sequence of callable objects that accepts
(env, agent, evaluator, step, eval_score) as arguments. They are
called every evaluation. See pfrl.experiments.evaluation_hooks.
save_best_so_far_agent (bool): If set to True, after each evaluation,
if the score (= mean return of evaluation episodes) exceeds
the best-so-far score, the current agent is saved.
use_tensorboard (bool): Additionally log eval stats to tensorboard
logger (logging.Logger): Logger used in this function.
Returns:
agent: Trained agent.
eval_stats_history: List of evaluation episode stats dict.
"""
logger = logger or logging.getLogger(__name__)
os.makedirs(outdir, exist_ok=True)
if eval_env is None:
eval_env = env
if eval_max_episode_len is None:
eval_max_episode_len = max_episode_len
evaluator = Evaluator(
agent=agent,
n_steps=eval_n_steps,
n_episodes=eval_n_episodes,
eval_interval=eval_interval,
outdir=outdir,
max_episode_len=eval_max_episode_len,
env=eval_env,
step_offset=step_offset,
save_best_so_far_agent=save_best_so_far_agent,
use_tensorboard=use_tensorboard,
logger=logger,
)
eval_stats_history = train_agent_batch(
agent,
env,
steps,
outdir,
checkpoint_freq=checkpoint_freq,
max_episode_len=max_episode_len,
step_offset=step_offset,
evaluator=evaluator,
successful_score=successful_score,
return_window_size=return_window_size,
log_interval=log_interval,
step_hooks=step_hooks,
evaluation_hooks=evaluation_hooks,
logger=logger,
)
return agent, eval_stats_history
def train_agent_with_evaluation(
agent,
env,
steps,
eval_n_steps,
eval_n_episodes,
eval_interval,
outdir,
checkpoint_freq=None,
train_max_episode_len=None,
step_offset=0,
eval_max_episode_len=None,
eval_env=None,
successful_score=None,
step_hooks=(),
evaluation_hooks=(),
save_best_so_far_agent=True,
use_tensorboard=False,
eval_during_episode=False,
logger=None,
):
"""Train an agent while periodically evaluating it.
Args:
agent: A pfrl.agent.Agent
env: Environment train the agent against.
steps (int): Total number of timesteps for training.
eval_n_steps (int): Number of timesteps at each evaluation phase.
eval_n_episodes (int): Number of episodes at each evaluation phase.
eval_interval (int): Interval of evaluation.
outdir (str): Path to the directory to output data.
checkpoint_freq (int): frequency at which agents are stored.
train_max_episode_len (int): Maximum episode length during training.
step_offset (int): Time step from which training starts.
eval_max_episode_len (int or None): Maximum episode length of
evaluation runs. If None, train_max_episode_len is used instead.
eval_env: Environment used for evaluation.
successful_score (float): Finish training if the mean score is greater
than or equal to this value if not None
step_hooks (Sequence): Sequence of callable objects that accepts
(env, agent, step) as arguments. They are called every step.
See pfrl.experiments.hooks.
evaluation_hooks (Sequence): Sequence of
pfrl.experiments.evaluation_hooks.EvaluationHook objects. They are
called after each evaluation.
save_best_so_far_agent (bool): If set to True, after each evaluation
phase, if the score (= mean return of evaluation episodes) exceeds
the best-so-far score, the current agent is saved.
use_tensorboard (bool): Additionally log eval stats to tensorboard
eval_during_episode (bool): Allow running evaluation during training episodes.
This should be enabled only when `env` and `eval_env` are independent.
logger (logging.Logger): Logger used in this function.
Returns:
agent: Trained agent.
eval_stats_history: List of evaluation episode stats dict.
"""
logger = logger or logging.getLogger(__name__)
for hook in evaluation_hooks:
if not hook.support_train_agent:
raise ValueError(
"{} does not support train_agent_with_evaluation().".format(hook)
)
os.makedirs(outdir, exist_ok=True)
if eval_env is None:
assert not eval_during_episode, (
"To run evaluation during training episodes, you need to specify `eval_env`"
" that is independent from `env`."
)
eval_env = env
if eval_max_episode_len is None:
eval_max_episode_len = train_max_episode_len
evaluator = Evaluator(
agent=agent,
n_steps=eval_n_steps,
n_episodes=eval_n_episodes,
eval_interval=eval_interval,
outdir=outdir,
max_episode_len=eval_max_episode_len,
env=eval_env,
step_offset=step_offset,
evaluation_hooks=evaluation_hooks,
save_best_so_far_agent=save_best_so_far_agent,
use_tensorboard=use_tensorboard,
logger=logger,
)
eval_stats_history = train_agent(
agent,
env,
steps,
outdir,
checkpoint_freq=checkpoint_freq,
max_episode_len=train_max_episode_len,
step_offset=step_offset,
evaluator=evaluator,
successful_score=successful_score,
step_hooks=step_hooks,
eval_during_episode=eval_during_episode,
logger=logger,
)
return agent, eval_stats_history
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--outdir",
type=str,
default="results",
help=(
"Directory path to save output files."
" If it does not exist, it will be created."
),
)
parser.add_argument(
"--env",
type=str,
default="'DClawTurnFixed-v0'",
help="OpenAI Gym MuJoCo env to perform algorithm on.",
)
parser.add_argument("--seed", type=int, default=0, help="Random seed [0, 2 ** 32)")
parser.add_argument(
"--gpu", type=int, default=-1, help="GPU to use, set to -1 if no GPU."
)
parser.add_argument(
"--load", type=str, default="", help="Directory to load agent from."
)
parser.add_argument(
"--max-steps",
type=int,
default=10 ** 6,
help="Total number of timesteps to train the agent.",
)
parser.add_argument(
"--eval-n-runs",
type=int,
default=10,
help="Number of episodes run for each evaluation.",
)
parser.add_argument(
"--eval-interval",
type=int,
default=5000,
help="Interval in timesteps between evaluations.",
)
parser.add_argument(
"--replay-start-size",
type=int,
default=10000,
help="Minimum replay buffer size before " + "performing gradient updates.",
)
parser.add_argument("--batch-size", type=int, default=64, help="Minibatch size")
parser.add_argument(
"--render", action="store_true", help="Render env states in a GUI window."
)
parser.add_argument(
"--demo", action="store_true", help="Just run evaluation, not training."
)
parser.add_argument("--load-pretrained", action="store_true", default=False)
parser.add_argument(
"--pretrained-type", type=str, default="best", choices=["best", "final"]
)
parser.add_argument(
"--monitor", action="store_true", help="Wrap env with gym.wrappers.Monitor."
)
parser.add_argument(
"--log-level", type=int, default=logging.INFO, help="Level of the root logger."
)
parser.add_argument("--gamma", type=float, default=0.9)
parser.add_argument("--ddpg-training-steps", type=int, default=int(1e3))
parser.add_argument("--adversary-training-steps", type=int,default=int(1e3))
args = parser.parse_args()
logging.basicConfig(level=args.log_level)
args.outdir = './results'
print("Output files are saved in {}".format(args.outdir))
# Set a random seed used in PFRL
utils.set_random_seed(args.seed)
def make_env(test):
env = gym.make('DClawTurnFixed-v0')
# Unwrap TimeLimit wrapper
assert isinstance(env, gym.wrappers.TimeLimit)
env = env.env
# Use different random seeds for train and test envs
env_seed = 2 ** 32 - 1 - args.seed if test else args.seed
env.seed(env_seed)
# Cast observations to float32 because our model uses float32
env = pfrl.wrappers.CastObservationToFloat32(env)
if args.monitor:
env = pfrl.wrappers.Monitor(env, args.outdir)
if args.render and not test:
env = pfrl.wrappers.Render(env)
return env
env = make_env(test=False)
timestep_limit = env.spec.max_episode_steps
obs_space = env.observation_space
action_space = env.action_space
print("Observation space:", obs_space)
print("Action space:", action_space)
obs_size = obs_space.low.size
action_size = action_space.low.size
q_func = nn.Sequential(
ConcatObsAndAction(),
nn.Linear(obs_size + action_size, 256),
nn.ReLU(),
nn.Linear(256, 256),
nn.ReLU(),
nn.Linear(256,256),
nn.ReLU(),
nn.Linear(256, 1),
)
policy = nn.Sequential(
nn.Linear(obs_size, 256),
nn.ReLU(),
nn.Linear(256, 256),
nn.ReLU(),
nn.Linear(256,256),
nn.ReLU(),
nn.Linear(256, action_size),
BoundByTanh(low=action_space.low, high=action_space.high),
DeterministicHead(),
)
ddpg_opt_a = torch.optim.Adam(policy.parameters())
ddpg_opt_c = torch.optim.Adam(q_func.parameters())
ddpg_rbuf = replay_buffers.ReplayBuffer(10 ** 6)
ddpg_explorer = explorers.AdditiveGaussian(
scale=0.1, low=action_space.low, high=action_space.high
)
def ddpg_burnin_action_func():
"""Select random actions until model is updated one or more times."""
return np.random.uniform(action_space.low, action_space.high).astype(np.float32)
# Hyperparameters in http://arxiv.org/abs/1802.09477
ddpg_agent = DDPG(
policy,
q_func,
ddpg_opt_a,
ddpg_opt_c,
ddpg_rbuf,
gamma=args.gamma,
explorer=ddpg_explorer,
replay_start_size=args.replay_start_size,
target_update_method="soft",
target_update_interval=1,
update_interval=1,
soft_update_tau=5e-3,
n_times_update=1,
gpu=args.gpu,
minibatch_size=args.batch_size,
burnin_action_func=ddpg_burnin_action_func,
)
def adversary_random_func():
return np.random.randint(0,9)
# adversary_q = Critic(obs_size, 1, hidden_size=adversary_hidden_size)
# adversary_action_space = gym.spaces.discrete.Discrete(9)
# adversary_q = q_functions.FCQuadraticStateQFunction(
# obs_size, 1, n_hidden_channels = 256, n_hidden_layers = 2,action_space = adversary_action_space
# )
adversary_q = nn.Sequential(
nn.Linear(obs_size, 256),
nn.Linear(256,256),
nn.Linear(256,256),
nn.Linear(256,1),
DiscreteActionValueHead(),
)
adversary_optimizer = torch.optim.Adam(adversary_q.parameters(), lr=1e-3)
adversary_rbuf_capacity = int(1e6)
adversary_rbuf = replay_buffers.ReplayBuffer(adversary_rbuf_capacity)
adversary_explorer = explorers.LinearDecayEpsilonGreedy(
1.0, 0.1, 10**4, adversary_random_func
)
adversary_agent = DQN(
adversary_q,
adversary_optimizer,
adversary_rbuf,
gpu=args.gpu,
gamma=args.gamma,
explorer=adversary_explorer,
replay_start_size=args.replay_start_size,
target_update_interval=1,
minibatch_size=args.batch_size,
target_update_method='soft',
soft_update_tau=5e-3
)
logger = logging.getLogger(__name__)
eval_env = make_env(test=True)
evaluator = Evaluator(
agent=ddpg_agent,
n_steps=None,
n_episodes=args.eval_n_runs,
eval_interval=args.eval_interval,
outdir=args.outdir,
max_episode_len=timestep_limit,
env=eval_env,
step_offset=0,
save_best_so_far_agent=True,
use_tensorboard=True,
logger=logger,
)
episode_reward = 0
ddpg_episode_idx = 0
adversary_episode_idx = 0
# o_0, r_0
current_state = env.reset()
t = 0
ddpg_t = 0
adversary_t = 0
episode_len = 0
try:
while t < args.max_steps:
for i in range(args.ddpg_training_steps):
t += 1
ddpg_t += 1
ddpg_action = ddpg_agent.act(current_state)
adversary_action = adversary_agent.act(current_state)
ddpg_action[adversary_action] = 0
next_state, reward, done, info = env.step(ddpg_action)
episode_reward += reward
episode_len += 1
reset = episode_len == timestep_limit or info.get("needs_reset", False)
ddpg_agent.observe(next_state, reward, done, reset)
current_state = next_state
if done or reset or t == args.max_steps:
logger.info(
"ddpg phase: outdir:%s step:%s episode:%s R:%s",
args.outdir,
ddpg_t,
ddpg_episode_idx,
episode_reward,
)
logger.info("statistics:%s", ddpg_agent.get_statistics())
if evaluator is not None:
evaluator.evaluate_if_necessary(t=t, episodes=ddpg_episode_idx + 1)
if t == args.max_steps:
break
episode_reward = 0
ddpg_episode_idx += 1
episode_len = 0
current_state = env.reset()
episode_reward = 0
episode_len = 0
current_state = env.reset()
print("start adversary training ")
for i in range(args.adversary_training_steps):
t += 1
adversary_t += 1
ddpg_action = ddpg_agent.act(current_state)
adversary_action = adversary_agent.act(current_state)
ddpg_action[adversary_action] = 0
next_state, reward, done, info = env.step(ddpg_action)
reward = -reward
episode_len += 1
reset = episode_len == timestep_limit or info.get("needs_reset", False)
adversary_agent.observe(next_state, reward, done, reset)
current_state = next_state
if done or reset or t == args.max_steps:
if t == args.max_steps:
break
episode_reward = 0
adversary_episode_idx += 1
episode_len = 0
current_state = env.reset()
except (Exception, KeyboardInterrupt):
# Save the current model before being killed
save_agent(ddpg_agent, t, args.outdir, logger, suffix="_ddpg_except")
save_agent(adversary_agent, t, args.outdir, logger, suffix="_adversary_except" )
raise
# Save the final model
save_agent(ddpg_agent, t, args.outdir, logger, suffix="_ddpg_finish")
save_agent(adversary_agent, t, args.outdir, logger, suffix="_adversary_finish" )
# if args.demo:
# eval_env.render()
# eval_stats = experiments.eval_performance(
# env=eval_env,
# agent=ddpg_agent,
# n_steps=None,
# n_episodes=args.eval_n_runbase_envs,
# max_episode_len=timestep_limit,
# )
# print(
# "n_runs: {} mean: {} median: {} stdev {}".format(
# args.eval_n_runs,
# eval_stats["mean"],
# eval_stats["median"],
# eval_stats["stdev"],
# )
# )
# else:
# experiments.train_agent_with_evaluation(
# agent=ddpg_agent,
# env=env,
# steps=args.steps,
# eval_env=eval_env,
# eval_n_steps=None,
# eval_n_episodes=args.eval_n_runs,
# eval_interval=args.eval_interval,
# outdir=args.outdir,
# train_max_episode_len=timestep_limit,
# )
print("finish")