def __init__(self, config: Dict[str, any], result_dir: str, cache_stats: CacheInformation):
super().__init__(config, result_dir, cache_stats)
# evaluation specific variables
self.observation_seen = 0
self.episode_reward = 0
self.checkpoint_steps = config['checkpoint_steps']
self._incomplete_experiences = TTLCache(InMemoryStorage())
self._incomplete_experiences.expired_entry_callback(self._observe_expired_incomplete_experience)
self.experimental_reward = config.get('experimental_reward', False)
agent_config = config['agent_config']
self.converter = CachingStrategyRLConverter()
# action space: should cache: true or false
# state space: [capacity (1), query key(1), query result set(num_indexes)]
fields_in_state = len(CachingAgentSystemState.__slots__)
self.agent = Agent.from_spec(agent_config,
state_space=FloatBox(shape=(fields_in_state,)),
action_space=IntBox(2))
self.logger = logging.getLogger(__name__)
name = 'rl_caching_strategy'
self.reward_logger = create_file_logger(name=f'{name}_reward_logger', result_dir=self.result_dir)
self.loss_logger = create_file_logger(name=f'{name}_loss_logger', result_dir=self.result_dir)
self.observation_logger = create_file_logger(name=f'{name}_observation_logger', result_dir=self.result_dir)
self.entry_hits_logger = create_file_logger(name=f'{name}_entry_hits_logger', result_dir=self.result_dir)
self.key_vocab = Vocabulary()
def test_dqn_on_pong(self):
"""
Creates a DQNAgent and runs it via a Runner on an openAI Pong Env.
"""
env = OpenAIGymEnv("Pong-v0",
frameskip=4,
max_num_noops=30,
episodic_life=True,
visualize=False)
agent_config = config_from_path("configs/dqn_agent_for_pong.json")
preprocessing_spec = agent_config.pop("preprocessor_spec")
agent = Agent.from_spec(
# Uses 2015 DQN parameters as closely as possible.
agent_config,
state_space=self.pong_preprocessed_state_space,
# Try with "reduced" action space (actually only 3 actions, up, down, no-op)
action_space=env.action_space)
time_steps = 4000000
worker = SingleThreadedWorker(env_spec=lambda: env,
agent=agent,
render=True,
preprocessing_spec=preprocessing_spec,
worker_executes_preprocessing=True)
results = worker.execute_timesteps(time_steps, use_exploration=True)
def test_individual_env(self):
env = Environment.from_spec(self.env_spec)
agent = Agent.from_spec(
# Uses 2015 DQN parameters as closely as possible.
config_from_path("configs/dqn_agent_for_pong.json"),
state_space=env.state_space,
# Try with "reduced" action space (actually only 3 actions, up, down, no-op)
action_space=env.action_space)
state = env.reset()
start = time.monotonic()
ep_length = 0
for _ in range_(self.samples):
action = agent.get_action(state)
state, reward, terminal, info = env.step(action)
ep_length += 1
if terminal:
print("reset after {} states".format(ep_length))
env.reset()
ep_length = 0
runtime = time.monotonic() - start
tp = self.samples / runtime
print('Testing individual env {} performance:'.format(
self.env_spec["gym_env"]))
print('Ran {} steps, throughput: {} states/s, total time: {} s'.format(
self.samples, tp, runtime))
def test_policy_and_vf_weight_syncing(self):
"""
Tests weight synchronization with a local agent and a remote worker.
"""
# First, create a local agent
env_spec = dict(type="openai", gym_env="CartPole-v0")
env = Environment.from_spec(env_spec)
agent_config = config_from_path("configs/sync_batch_ppo_cartpole.json")
ray_spec = agent_config["execution_spec"].pop("ray_spec")
local_agent = Agent.from_spec(agent_config,
state_space=env.state_space,
action_space=env.action_space)
ray_spec["worker_spec"]["worker_sample_size"] = 50
# Create a remote worker with the same agent config.
worker = RayPolicyWorker.as_remote().remote(agent_config,
ray_spec["worker_spec"],
self.env_spec,
auto_build=True)
# This imitates the initial executor sync without ray.put
weights = RayWeight(local_agent.get_weights())
print('Weight type in init sync = {}'.format(type(weights)))
print("Weights = ", weights)
worker.set_weights.remote(weights)
print('Init weight sync successful.')
# Replicate worker syncing steps as done in e.g. Ape-X executor:
weights = RayWeight(local_agent.get_weights())
print('Weight type returned by ray put = {}'.format(type(weights)))
print(weights)
ret = worker.set_weights.remote(weights)
ray.wait([ret])
print('Object store weight sync successful.')
def __init__(self, config: Dict[str, any], result_dir: str,
cache_stats: CacheInformation):
super().__init__(config, result_dir, cache_stats)
self.supported_observations = {
ObservationType.Hit, ObservationType.Miss,
ObservationType.Invalidate
}
# evaluation specific variables
self.observation_seen = 0
self.cum_reward = 0
self.checkpoint_steps = config['checkpoint_steps']
self._incomplete_experiences = TTLCache(InMemoryStorage())
self._incomplete_experiences.expired_entry_callback(
self._observe_expiry_eviction)
self.non_terminal_observations = {
ObservationType.EvictionPolicy, ObservationType.Expiration
}
agent_config = config['agent_config']
self.maximum_ttl = config['max_ttl']
fields_in_state = len(MultiTaskAgentSystemState.__slots__)
action_space = RLDict({
'ttl': IntBox(low=0, high=self.maximum_ttl),
'eviction': IntBox(low=0, high=2)
})
self.agent = Agent.from_spec(
agent_config,
state_space=FloatBox(shape=(fields_in_state, )),
action_space=action_space)
# TODO refactor into common RL interface for all strategies
self.logger = logging.getLogger(__name__)
name = 'rl_multi_strategy'
self.reward_logger = create_file_logger(name=f'{name}_reward_logger',
result_dir=self.result_dir)
self.loss_logger = create_file_logger(name=f'{name}_loss_logger',
result_dir=self.result_dir)
self.ttl_logger = create_file_logger(name=f'{name}_ttl_logger',
result_dir=self.result_dir)
self.observation_logger = create_file_logger(
name=f'{name}_observation_logger', result_dir=self.result_dir)
self.performance_logger = create_file_logger(
name=f'{name}_performance_logger', result_dir=self.result_dir)
self.key_vocab = Vocabulary()
def main(argv):
try:
FLAGS(argv)
except flags.Error as e:
print('%s\\nUsage: %s ARGS\\n%s' % (e, sys.argv[0], FLAGS))
agent_config_path = os.path.join(os.getcwd(), FLAGS.config)
with open(agent_config_path, 'rt') as fp:
agent_config = json.load(fp)
env = OpenAIGymEnv.from_spec({
"type": "openai",
"gym_env": FLAGS.env,
"visualize": FLAGS.visualize
})
agent = Agent.from_spec(agent_config,
state_space=env.state_space,
action_space=env.action_space)
episode_returns = []
def episode_finished_callback(episode_return, duration, timesteps,
**kwargs):
episode_returns.append(episode_return)
if len(episode_returns) % 10 == 0:
print("Episode {} finished: reward={:.2f}, average reward={:.2f}.".
format(len(episode_returns), episode_return,
np.mean(episode_returns[-10:])))
worker = SingleThreadedWorker(
env_spec=lambda: env,
agent=agent,
render=False,
worker_executes_preprocessing=False,
episode_finish_callback=episode_finished_callback)
print(
"Starting workload, this will take some time for the agents to build.")
# Use exploration is true for training, false for evaluation.
worker.execute_timesteps(20000, use_exploration=True)
# Note: A basic actor critic is very sensitive to hyper-parameters and might collapse after reaching the maximum
# reward. In practice, it would be recommended to stop training when a reward threshold is reached.
print("Mean reward: {:.2f} / over the last 10 episodes: {:.2f}".format(
np.mean(episode_returns), np.mean(episode_returns[-10:])))
def test_update_throughput(self):
env = Environment.from_spec(self.env_spec)
# TODO comment in for multi gpu
# config_from_path("configs/multi_gpu_ray_apex_for_pong.json"),
config = config_from_path("configs/ray_apex_for_pong.json")
# Adjust to usable GPUs for test system.
num_gpus = [1]
for gpu_count in num_gpus:
config["execution_spec"]["gpu_spec"]["num_gpus"] = gpu_count
config["execution_spec"]["gpu_spec"]["per_process_gpu_memory_fraction"] = 1.0 / gpu_count
agent = Agent.from_spec(
# TODO replace with config from above
config_from_path("configs/ray_apex_for_pong.json"),
state_space=env.state_space,
# Try with "reduced" action space (actually only 3 actions, up, down, no-op)
action_space=env.action_space
)
batch_space = Dict(
states=agent.preprocessed_state_space,
actions=env.action_space,
rewards=FloatBox(),
next_states=agent.preprocessed_state_space,
terminals=IntBox(low=0, high=1),
importance_weights=FloatBox(),
add_batch_rank=True
)
batch_size = 512 * gpu_count
num_samples = 50
samples = [batch_space.sample(batch_size) for _ in range(num_samples)]
times = []
throughputs = []
for sample in samples:
start = time.perf_counter()
agent.update(sample)
runtime = time.perf_counter() - start
times.append(runtime)
throughputs.append(batch_size / runtime)
print("Throughput: {} samples / s ({}) for {} GPUs".format(np.mean(throughputs),
np.std(throughputs), gpu_count))
def test_worker_weight_syncing(self):
"""
Tests weight synchronization with a local agent and a remote worker.
"""
# First, create a local agent
env_spec = dict(
type="openai",
gym_env="PongNoFrameskip-v4",
# The frameskip in the agent config will trigger worker skips, this
# is used for internal env.
frameskip=4,
max_num_noops=30,
episodic_life=True)
env = Environment.from_spec(env_spec)
agent_config = config_from_path("configs/ray_apex_for_pong.json")
# Remove unneeded apex params.
if "apex_replay_spec" in agent_config:
agent_config.pop("apex_replay_spec")
ray_spec = agent_config["execution_spec"].pop("ray_spec")
local_agent = Agent.from_spec(agent_config,
state_space=env.state_space,
action_space=env.action_space)
# Create a remote worker with the same agent config.
worker = RayWorker.as_remote().remote(agent_config,
ray_spec["worker_spec"],
self.env_spec,
auto_build=True)
# This imitates the initial executor sync without ray.put
weights = local_agent.get_weights()
print('Weight type in init sync = {}'.format(type(weights)))
worker.set_weights.remote(weights["policy_weights"],
weights["value_function_weights"])
print('Init weight sync successful.')
# Replicate worker syncing steps as done in e.g. Ape-X executor:
weights = ray.put(local_agent.get_weights())
print('Weight type returned by ray put = {}'.format(type(weights)))
print(weights)
worker.set_weights.remote(weights["policy_weights"],
weights["value_function_weights"])
print('Object store weight sync successful.')
def main(argv):
try:
FLAGS(argv)
except flags.Error as e:
print('%s\\nUsage: %s ARGS\\n%s' % (e, sys.argv[0], FLAGS))
agent_config_path = os.path.join(os.getcwd(), FLAGS.config)
with open(agent_config_path, 'rt') as fp:
agent_config = json.load(fp)
env = OpenAIGymEnv.from_spec({
"type": "openai",
"gym_env": FLAGS.env
})
print(env.state_space)
agent = Agent.from_spec(
agent_config,
state_space=env.state_space,
action_space=env.action_space
)
episode_returns = []
def episode_finished_callback(episode_return, duration, timesteps, **kwargs):
episode_returns.append(episode_return)
if len(episode_returns) % 5 == 0:
print("Episode {} finished: reward={:.2f}, average reward={:.2f}.".format(
len(episode_returns), episode_return, np.mean(episode_returns[-5:])
))
worker = SingleThreadedWorker(env_spec=lambda: env, agent=agent, render=FLAGS.render, worker_executes_preprocessing=False,
episode_finish_callback=episode_finished_callback)
print("Starting workload, this will take some time for the agents to build.")
worker.execute_episodes(100, use_exploration=True)
# Use exploration is true for training, false for evaluation.
#worker.execute_episodes(100, use_exploration=False)
print("Mean reward: {:.2f} / over the last 10 episodes: {:.2f}".format(
np.mean(episode_returns), np.mean(episode_returns[-10:])
))
def test_apex_weight_syncing(self):
env = RandomEnv(state_space=spaces.IntBox(2),
action_space=spaces.IntBox(2),
deterministic=True)
agent = Agent.from_spec(
config_from_path("configs/apex_agent_for_random_env.json"),
state_space=env.state_space,
action_space=env.action_space)
policy_weights = agent.get_policy_weights()
print('policy weights: {}'.format(policy_weights))
for variable, weights in policy_weights.items():
weights += 0.01
agent.set_policy_weights(policy_weights)
new_weights = agent.get_policy_weights()
recursive_assert_almost_equal(policy_weights, new_weights)
def main(argv):
try:
FLAGS(argv)
except flags.Error as e:
print('%s\\nUsage: %s ARGS\\n%s' % (e, sys.argv[0], FLAGS))
agent_config_path = os.path.join(os.getcwd(), FLAGS.config)
with open(agent_config_path, 'rt') as fp:
agent_config = json.load(fp)
env = MLAgentsEnv()
agent = Agent.from_spec(agent_config,
state_space=env.state_space,
action_space=env.action_space)
episode_returns = []
def episode_finished_callback(episode_return, duration, timesteps,
**kwargs):
episode_returns.append(episode_return)
finished_episodes = len(episode_returns)
if finished_episodes % 4 == 0:
print(
"Episode {} finished in {:d}sec: total avg. reward={:.2f}; last 10 episodes={:.2f}; last "
"100 episodes={:.2f}".format(
finished_episodes, int(duration), np.mean(episode_returns),
np.mean(episode_returns[-min(finished_episodes, 10):]),
np.mean(episode_returns[-min(finished_episodes, 100):])))
worker = SingleThreadedWorker(
env_spec=env,
agent=agent,
render=False,
worker_executes_preprocessing=False,
episode_finish_callback=episode_finished_callback)
print(
"Starting workload, this will take some time for the agents to build.")
# Use exploration is true for training, false for evaluation.
worker.execute_timesteps(500000, use_exploration=True)
print("Mean reward: {:.2f} / over the last 10 episodes: {:.2f}".format(
np.mean(episode_returns), np.mean(episode_returns[-10:])))
def main(argv):
try:
FLAGS(argv)
except flags.Error as e:
print('%s\\nUsage: %s ARGS\\n%s' % (e, sys.argv[0], FLAGS))
agent_config_path = os.path.join(os.getcwd(), FLAGS.config)
with open(agent_config_path, 'rt') as fp:
agent_config = json.load(fp)
# Override openAI gym env per command line.
if FLAGS.env is None:
env_spec = agent_config["environment_spec"]
else:
env_spec = dict(type="openai-gym", gym_env=FLAGS.env)
# Override number of visualized envs per command line.
if FLAGS.visualize != -1:
env_spec["visualize"] = FLAGS.visualize
dummy_env = OpenAIGymEnv.from_spec(env_spec)
agent = Agent.from_spec(
agent_config,
state_space=dummy_env.state_space,
action_space=dummy_env.action_space
)
dummy_env.terminate()
learn_updates = 6000
mean_returns = []
for i in range(learn_updates):
ret = agent.update()
mean_return = _calc_mean_return(ret)
mean_returns.append(mean_return)
print("Iteration={} Loss={:.4f} Avg-reward={:.2f}".format(i, float(ret[1]), mean_return))
print("Mean return: {:.2f} / over the last 10 episodes: {:.2f}".format(
np.nanmean(mean_returns), np.nanmean(mean_returns[-10:])
))
time.sleep(1)
agent.terminate()
time.sleep(3)
def test_weights_getting_setting(self):
"""
Tests getting and setting of the Agent's weights.
"""
env = GridWorld(world="2x2")
agent = Agent.from_spec(
config_from_path("configs/dqn_agent_for_functionality_test.json"),
state_space=env.state_space,
action_space=env.action_space)
weights = agent.get_weights()
new_weights = {}
for key, weight in weights["policy_weights"].items():
new_weights[key] = weight + 0.01
agent.set_weights(new_weights)
new_actual_weights = agent.get_weights()
recursive_assert_almost_equal(new_actual_weights["policy_weights"],
new_weights)
def test_apex_weight_syncing(self):
agent_config = config_from_path("configs/ray_apex_for_pong.json")
agent_config["execution_spec"].pop("ray_spec")
environment = OpenAIGymEnv("Pong-v0", frameskip=4)
agent = Agent.from_spec(
agent_config,
state_space=environment.state_space,
action_space=environment.action_space
)
weights = agent.get_weights()["policy_weights"]
print("type weights = ", type(weights))
for variable, value in weights.items():
print("Type value = ", type(value))
value += 0.01
agent.set_weights(weights)
new_weights = agent.get_weights()["policy_weights"]
recursive_assert_almost_equal(weights, new_weights)
def main(argv):
try:
FLAGS(argv)
except flags.Error as e:
print('%s\\nUsage: %s ARGS\\n%s' % (e, sys.argv[0], FLAGS))
agent_config = read_config_file(FLAGS.config)
env = OpenAIGymEnv.from_spec({
"type": "openai",
"gym_env": FLAGS.env
})
agent = Agent.from_spec(
agent_config,
summary_spec=dict(
summary_regexp=FLAGS.summary_regexp
),
state_space=env.state_space,
action_space=env.action_space
)
rewards = []
def episode_finished_callback(reward, duration, timesteps, **kwargs):
rewards.append(reward)
if len(rewards) % 10 == 0:
print("Episode {} finished: reward={:.2f}, average reward={:.2f}.".format(
len(rewards), reward, np.mean(rewards[-10:])
))
worker = SingleThreadedWorker(
env_spec=lambda: env, agent=agent, render=FLAGS.render, worker_executes_preprocessing=False,
episode_finish_callback=episode_finished_callback
)
print("Starting workload, this will take some time for the agents to build.")
results = worker.execute_episodes(200, use_exploration=True)
print("Mean reward: {:.2f} / over the last 10 episodes: {:.2f}".format(
np.mean(rewards), np.mean(rewards[-10:])
))
def run_experiment(self, environment, experiment_num=0):
environment = RLgraphEnvironmentWrapper(environment)
environment.add_episode_end_callback(self.episode_finished,
environment,
runner_id=1)
config = copy(self.config)
max_episodes = config.pop('max_episodes', None)
max_timesteps = config.pop('max_timesteps', None)
max_episode_timesteps = config.pop('max_episode_timesteps')
agent = Agent.from_spec(
spec=config,
state_space=environment.state_space,
action_space=environment.action_space,
)
if experiment_num == 0 and self.load_model_file:
logging.info("Loading model data from file: {}".format(
self.load_model))
agent.load_model(self.load_model_file)
runner = SingleThreadedWorker(agent=agent, environment=environment)
environment.reset()
agent.reset_buffers()
if max_timesteps:
runner.execute_timesteps(
num_timesteps=max_timesteps,
max_timesteps_per_episode=max_episode_timesteps)
else:
runner.execute_episodes(
num_episodes=max_episodes,
max_timesteps_per_episode=max_episode_timesteps)
return dict(initial_reset_time=0,
episode_rewards=runner.episode_rewards,
episode_timesteps=runner.episode_steps,
episode_end_times=runner.episode_durations)
def test_update_from_external(self):
agent_config = config_from_path("configs/ray_apex_for_pong.json")
agent_config["execution_spec"].pop("ray_spec")
environment = OpenAIGymEnv("Pong-v0", frameskip=4)
agent = Agent.from_spec(
agent_config,
state_space=environment.state_space,
action_space=environment.action_space
)
batch = {
"states": agent.preprocessed_state_space.sample(200),
"actions": environment.action_space.sample(200),
"rewards": np.zeros(200, dtype=np.float32),
"terminals": [False] * 200,
"next_states": agent.preprocessed_state_space.sample(200),
"importance_weights": np.ones(200, dtype=np.float32)
}
agent.update(batch)
def __init__(self, config: Dict[str, any], result_dir: str,
cache_stats: CacheInformation):
super().__init__(config, result_dir, cache_stats)
# evaluation specific variables
self.observation_seen = 0
self.episode_reward = 0
self.checkpoint_steps = config['checkpoint_steps']
self._incomplete_experiences = TTLCache(InMemoryStorage())
self._incomplete_experiences.expired_entry_callback(
self._observe_expired_incomplete_experience)
self.view_of_the_cache = {} # type: Dict[str, Dict[str, any]]
self._end_episode_observation = {
ObservationType.Invalidate, ObservationType.Miss,
ObservationType.Expiration
}
# TODO refactor into common RL interface for all strategies
# Agent configuration (can be shared with others)
agent_config = config['agent_config']
fields_in_state = len(EvictionAgentSystemState.__slots__)
self.converter = EvictionStrategyRLConverter(self.result_dir)
# State: fields to observe in question
# Action: to evict or not that key
self.agent = Agent.from_spec(
agent_config,
state_space=FloatBox(shape=(fields_in_state, )),
action_space=IntBox(low=0, high=2))
self.logger = logging.getLogger(__name__)
name = 'rl_eviction_strategy'
self.reward_logger = create_file_logger(name=f'{name}_reward_logger',
result_dir=self.result_dir)
self.loss_logger = create_file_logger(name=f'{name}_loss_logger',
result_dir=self.result_dir)
self.observation_logger = create_file_logger(
name=f'{name}_observation_logger', result_dir=self.result_dir)
self.key_vocab = Vocabulary()
def main(argv):
try:
FLAGS(argv)
except flags.Error as e:
print('%s\\nUsage: %s ARGS\\n%s' % (e, sys.argv[0], FLAGS))
agent_config_path = os.path.join(os.getcwd(), FLAGS.config)
with open(agent_config_path, 'rt') as fp:
agent_config = json.load(fp)
env = OpenAIGymEnv.from_spec({"type": "openai", "gym_env": FLAGS.env})
agent = Agent.from_spec(
# Uses 2015 DQN parameters as closely as possible.
agent_config,
state_space=env.state_space,
# Try with "reduced" action space (actually only 3 actions, up, down, no-op)
action_space=env.action_space)
rewards = []
def episode_finished_callback(reward, duration, timesteps, **kwargs):
rewards.append(reward)
if len(rewards) % 10 == 0:
print("Episode {} finished: reward={:.2f}, average reward={:.2f}.".
format(len(rewards), reward, np.mean(rewards[-10:])))
worker = SingleThreadedWorker(
env_spec=lambda: env,
agent=agent,
render=False,
worker_executes_preprocessing=False,
episode_finish_callback=episode_finished_callback)
print(
"Starting workload, this will take some time for the agents to build.")
results = worker.execute_episodes(200, use_exploration=True)
print("Mean reward: {:.2f} / over the last 10 episodes: {:.2f}".format(
np.mean(rewards), np.mean(rewards[-10:])))
def main(argv):
try:
FLAGS(argv)
except flags.Error as e:
print('%s\\nUsage: %s ARGS\\n%s' % (e, sys.argv[0], FLAGS))
agent_config_path = os.path.join(os.getcwd(), FLAGS.config)
with open(agent_config_path, 'rt') as fp:
agent_config = json.load(fp)
env = MLAgentsEnv()
agent = Agent.from_spec(agent_config,
state_space=env.state_space,
action_space=env.action_space)
rewards = []
def episode_finished_callback(reward, duration, timesteps, **kwargs):
rewards.append(reward)
if len(rewards) % 10 == 0:
print("Episode {} finished: reward={:.2f}, average reward={:.2f}.".
format(len(rewards), reward, np.mean(rewards[-10:])))
worker = SingleThreadedWorker(
env_spec=env,
agent=agent,
render=False,
worker_executes_preprocessing=False,
#synchronous_reset=True,
episode_finish_callback=episode_finished_callback)
print(
"Starting workload, this will take some time for the agents to build.")
# Use exploration is true for training, false for evaluation.
worker.execute_timesteps(100000, use_exploration=True)
print("Mean reward: {:.2f} / over the last 10 episodes: {:.2f}".format(
np.mean(rewards), np.mean(rewards[-10:])))
def __init__(self, config: Dict[str, any], result_dir: str,
cache_stats: CacheInformation):
super().__init__(config, result_dir, cache_stats)
self.observation_seen = 0
self.cum_reward = 0
self.checkpoint_steps = config['checkpoint_steps']
self._incomplete_experiences = TTLCache(InMemoryStorage())
self._incomplete_experiences.expired_entry_callback(
self._observe_expiry_eviction)
self.non_terminal_observations = {
ObservationType.EvictionPolicy, ObservationType.Expiration
}
agent_config = config['agent_config']
self.maximum_ttl = config['max_ttl']
self.experimental_reward = config.get('experimental_reward', False)
fields_in_state = len(TTLAgentSystemState.__slots__)
self.agent = Agent.from_spec(
agent_config,
state_space=FloatBox(shape=(fields_in_state, )),
action_space=FloatBox(low=0, high=self.maximum_ttl, shape=(1, )))
# TODO refactor into common RL interface for all strategies
self.logger = logging.getLogger(__name__)
name = 'rl_ttl_strategy'
self.reward_logger = create_file_logger(name=f'{name}_reward_logger',
result_dir=self.result_dir)
self.loss_logger = create_file_logger(name=f'{name}_loss_logger',
result_dir=self.result_dir)
self.ttl_logger = create_file_logger(name=f'{name}_ttl_logger',
result_dir=self.result_dir)
self.observation_logger = create_file_logger(
name=f'{name}_observation_logger', result_dir=self.result_dir)
self.key_vocab = Vocabulary()
self.errors = create_file_logger(name=f'{name}_error_logger',
result_dir=self.result_dir)
def test_sequential_vector_env(self):
vector_env = SequentialVectorEnv(num_environments=self.num_vector_envs,
env_spec=self.env_spec,
num_background_envs=2)
agent = Agent.from_spec(
# Uses 2015 DQN parameters as closely as possible.
config_from_path("configs/dqn_vector_env.json"),
state_space=vector_env.state_space,
# Try with "reduced" action space (actually only 3 actions, up, down, no-op)
action_space=vector_env.action_space)
states = vector_env.reset_all()
start = time.monotonic()
ep_lengths = [0 for _ in range_(self.num_vector_envs)]
for _ in range_(int(self.samples / self.num_vector_envs)):
# Sample all envs at once.
actions, preprocessed_states = agent.get_action(
states, extra_returns="preprocessed_states")
states, rewards, terminals, infos = vector_env.step(actions)
ep_lengths = [ep_length + 1 for ep_length in ep_lengths]
for i, terminal in enumerate(terminals):
if terminal:
print("reset env {} after {} states".format(
i, ep_lengths[i]))
vector_env.reset(i)
ep_lengths[i] = 0
runtime = time.monotonic() - start
tp = self.samples / runtime
print('Testing vector env {} performance:'.format(
self.env_spec["gym_env"]))
print('Ran {} steps, throughput: {} states/s, total time: {} s'.format(
self.samples, tp, runtime))
def setup_execution(self):
# Create local worker agent according to spec.
# Extract states and actions space.
environment = None
if isinstance(self.environment_spec, dict):
environment = Environment.from_spec(self.environment_spec)
elif hasattr(self.environment_spec, '__call__'):
environment = self.environment_spec()
self.agent_config["state_space"] = environment.state_space
self.agent_config["action_space"] = environment.action_space
# Start Ray cluster and connect to it.
self.local_agent = Agent.from_spec(self.agent_config)
# Set up worker thread for performing updates.
self.update_worker = UpdateWorker(
agent=self.local_agent,
in_queue_size=self.executor_spec["learn_queue_size"])
self.ray_init()
# Create remote sample workers based on ray cluster spec.
self.num_replay_workers = self.executor_spec["num_replay_workers"]
self.num_sample_workers = self.executor_spec["num_sample_workers"]
self.logger.info("Initializing {} local replay memories.".format(
self.num_replay_workers))
# Update memory size for num of workers
shard_size = int(self.apex_replay_spec["memory_spec"]["capacity"] /
self.num_replay_workers)
self.apex_replay_spec["memory_spec"]["capacity"] = shard_size
self.logger.info("Shard size per memory: {}".format(
self.apex_replay_spec["memory_spec"]["capacity"]))
min_sample_size = self.apex_replay_spec["min_sample_memory_size"]
self.apex_replay_spec["min_sample_memory_size"] = int(
min_sample_size / self.num_replay_workers)
self.logger.info("Sampling for learning starts at: {}".format(
self.apex_replay_spec["min_sample_memory_size"]))
# Set sample batch size:
self.apex_replay_spec["sample_batch_size"] = self.agent_config[
"update_spec"]["batch_size"]
self.logger.info("Sampling batch size {}".format(
self.apex_replay_spec["sample_batch_size"]))
self.ray_local_replay_memories = create_colocated_ray_actors(
cls=RayMemoryActor.as_remote(
num_cpus=self.num_cpus_per_replay_actor),
config=self.apex_replay_spec,
num_agents=self.num_replay_workers)
# Create remote workers for data collection.
self.worker_spec["worker_sample_size"] = self.worker_sample_size
self.logger.info(
"Initializing {} remote data collection agents, sample size: {}".
format(self.num_sample_workers,
self.worker_spec["worker_sample_size"]))
self.ray_env_sample_workers = self.create_remote_workers(
RayValueWorker,
self.num_sample_workers,
self.agent_config,
# *args
self.worker_spec,
self.environment_spec,
self.worker_frame_skip)
self.init_tasks()
agent_config_path = os.path.abspath(os.path.dirname(
os.path.dirname(__file__))) + '/agents/ppoSmartPrimer_config.json'
with open(agent_config_path, 'rt') as fp:
agent_config = json.load(fp)
env = OpenAIGymEnv.from_spec({
"type":
"openai",
"gym_env":
'gym_SmartPrimer:SmartPrimer-realistic-v2'
})
agent = Agent.from_spec(agent_config,
state_space=env.state_space,
action_space=env.action_space)
episode_returns = []
def episode_finished_callback(episode_return, duration, timesteps, *args,
**kwargs):
episode_returns.append(episode_return)
if len(episode_returns) % 100 == 0:
print("Episode {} finished: reward={:.2f}, average reward={:.2f}.".
format(len(episode_returns), episode_return,
np.mean(episode_returns[-100:])))
worker = SingleThreadedWorker(
def test_dqn_functionality(self):
"""
Creates a DQNAgent and runs it for a few steps in a GridWorld to vigorously test
all steps of the learning process.
"""
env = GridWorld(world="2x2", save_mode=True) # no holes, just fire
agent = Agent.from_spec( # type: DQNAgent
config_from_path("configs/dqn_agent_for_functionality_test.json"),
double_q=True,
dueling_q=True,
state_space=env.state_space,
action_space=env.action_space,
discount=0.95)
worker = SingleThreadedWorker(
env_spec=lambda: GridWorld(world="2x2", save_mode=True),
agent=agent)
test = AgentTest(worker=worker)
# Helper python DQNLossFunc object.
loss_func = DQNLossFunction(backend="python",
double_q=True,
discount=agent.discount)
loss_func.when_input_complete(input_spaces=dict(loss_per_item=[
spaces.FloatBox(shape=(4, ), add_batch_rank=True),
spaces.IntBox(4, add_batch_rank=True),
spaces.FloatBox(add_batch_rank=True),
spaces.BoolBox(add_batch_rank=True),
spaces.FloatBox(shape=(4, ), add_batch_rank=True),
spaces.FloatBox(shape=(4, ), add_batch_rank=True)
]),
action_space=env.action_space)
matrix1_qnet = np.array([[0.9] * 2] * 4)
matrix2_qnet = np.array([[0.8] * 5] * 2)
matrix1_target_net = np.array([[0.9] * 2] * 4)
matrix2_target_net = np.array([[0.8] * 5] * 2)
a = self._calculate_action(0, matrix1_qnet, matrix2_qnet)
# 1st step -> Expect insert into python-buffer.
# action: up (0)
test.step(1, reset=True)
# Environment's new state.
test.check_env("state", 0)
# Agent's buffer.
test.check_agent("states_buffer", [[1.0, 0.0, 0.0, 0.0]],
key_or_index="env_0") # <- prev state (preprocessed)
test.check_agent("actions_buffer", [a], key_or_index="env_0")
test.check_agent("rewards_buffer", [-1.0], key_or_index="env_0")
test.check_agent("terminals_buffer", [False], key_or_index="env_0")
# Memory contents.
test.check_var("replay-memory/index", 0)
test.check_var("replay-memory/size", 0)
test.check_var("replay-memory/memory/states",
np.array([[0] * 4] * agent.memory.capacity))
test.check_var("replay-memory/memory/actions",
np.array([0] * agent.memory.capacity))
test.check_var("replay-memory/memory/rewards",
np.array([0] * agent.memory.capacity))
test.check_var("replay-memory/memory/terminals",
np.array([False] * agent.memory.capacity))
# Check policy and target-policy weights (should be the same).
test.check_var("dueling-policy/neural-network/hidden/dense/kernel",
matrix1_qnet)
test.check_var("target-policy/neural-network/hidden/dense/kernel",
matrix1_qnet)
test.check_var(
"dueling-policy/action-adapter-0/action-network/action-layer/dense/kernel",
matrix2_qnet)
test.check_var(
"target-policy/action-adapter-0/action-network/action-layer/dense/kernel",
matrix2_qnet)
# 2nd step -> expect insert into memory (and python buffer should be empty again).
# action: up (0)
# Also check the policy and target policy values (Should be equal at this point).
test.step(1)
test.check_env("state", 0)
test.check_agent("states_buffer", [], key_or_index="env_0")
test.check_agent("actions_buffer", [], key_or_index="env_0")
test.check_agent("rewards_buffer", [], key_or_index="env_0")
test.check_agent("terminals_buffer", [], key_or_index="env_0")
test.check_var("replay-memory/index", 2)
test.check_var("replay-memory/size", 2)
test.check_var(
"replay-memory/memory/states",
np.array([[1.0, 0.0, 0.0, 0.0], [1.0, 0.0, 0.0, 0.0]] +
[[0.0, 0.0, 0.0, 0.0]] * (agent.memory.capacity - 2)))
test.check_var("replay-memory/memory/actions",
np.array([0, 0] + [0] * (agent.memory.capacity - 2)))
test.check_var(
"replay-memory/memory/rewards",
np.array([-1.0, -1.0] + [0.0] * (agent.memory.capacity - 2)))
test.check_var(
"replay-memory/memory/terminals",
np.array([False, True] + [False] * (agent.memory.capacity - 2)))
# Check policy and target-policy weights (should be the same).
test.check_var("dueling-policy/neural-network/hidden/dense/kernel",
matrix1_qnet)
test.check_var("target-policy/neural-network/hidden/dense/kernel",
matrix1_qnet)
test.check_var(
"dueling-policy/action-adapter-0/action-network/action-layer/dense/kernel",
matrix2_qnet)
test.check_var(
"target-policy/action-adapter-0/action-network/action-layer/dense/kernel",
matrix2_qnet)
# 3rd and 4th step -> expect another insert into memory (and python buffer should be empty again).
# actions: down (2), up (0) <- exploring is True = more random actions
# Expect an update to the policy variables (leave target as is (no sync yet)).
test.step(2, use_exploration=True)
test.check_env("state", 0)
test.check_agent("states_buffer", [], key_or_index="env_0")
test.check_agent("actions_buffer", [], key_or_index="env_0")
test.check_agent("rewards_buffer", [], key_or_index="env_0")
test.check_agent("terminals_buffer", [], key_or_index="env_0")
test.check_var("replay-memory/index", 4)
test.check_var("replay-memory/size", 4)
test.check_var(
"replay-memory/memory/states",
np.array([[1.0, 0.0, 0.0, 0.0]] * 3 + [[0.0, 1.0, 0.0, 0.0]] +
[[0.0, 0.0, 0.0, 0.0]] * (agent.memory.capacity - 4)))
test.check_var(
"replay-memory/memory/actions",
np.array([0, 0, 2, 0] + [0] * (agent.memory.capacity - 4)))
test.check_var(
"replay-memory/memory/rewards",
np.array([-1.0] * 4 + # + [-3.0] +
[0.0] * (agent.memory.capacity - 4)))
test.check_var(
"replay-memory/memory/terminals",
np.array([False, True] * 2 + [False] *
(agent.memory.capacity - 4)))
# Get the latest memory batch.
expected_batch = dict(states=np.array([[1.0, 0.0, 0.0, 0.0],
[1.0, 0.0, 0.0, 0.0]]),
actions=np.array([0, 1]),
rewards=np.array([-1.0, -3.0]),
terminals=np.array([False, True]),
next_states=np.array([[1.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0]]))
test.check_agent("last_memory_batch", expected_batch)
# Calculate the weight updates and check against actually update weights by the AgentDQN.
mat_updated = self._helper_update_matrix(expected_batch, matrix1_qnet,
matrix2_qnet,
matrix1_target_net,
matrix2_target_net, agent,
loss_func)
# Check policy and target-policy weights (policy should be updated now).
test.check_var("dueling-policy/neural-network/hidden/dense/kernel",
mat_updated[0],
decimals=4)
test.check_var("target-policy/neural-network/hidden/dense/kernel",
matrix1_target_net)
test.check_var(
"dueling-policy/action-adapter-0/action-network/action-layer/dense/kernel",
mat_updated[1],
decimals=4)
test.check_var(
"target-policy/action-adapter-0/action-network/action-layer/dense/kernel",
matrix2_target_net)
matrix1_qnet = mat_updated[0]
matrix2_qnet = mat_updated[1]
# 5th step -> Another buffer update check.
# action: down (2) (weights have been updated -> different actions)
test.step(1)
test.check_env("state", 3)
test.check_agent(
"states_buffer", [], key_or_index="env_0"
) # <- all empty b/c we reached end of episode (buffer gets force-flushed)
test.check_agent("actions_buffer", [], key_or_index="env_0")
test.check_agent("rewards_buffer", [], key_or_index="env_0")
test.check_agent("terminals_buffer", [], key_or_index="env_0")
test.check_agent("last_memory_batch", expected_batch)
test.check_var("replay-memory/index", 5)
test.check_var("replay-memory/size", 5)
test.check_var(
"replay-memory/memory/states",
np.array([[1.0, 0.0, 0.0, 0.0]] * 4 + [[0.0, 0.0, 1.0, 0.0]] +
[[0.0, 0.0, 0.0, 0.0]] * (agent.memory.capacity - 5)))
test.check_var("replay-memory/memory/actions",
np.array([0, 0, 0, 1, 2, 0]))
test.check_var("replay-memory/memory/rewards",
np.array([-1.0] * 3 + [-3.0, 1.0, 0.0]))
test.check_var("replay-memory/memory/terminals",
np.array([False, True] * 2 + [True, False]))
test.check_var("dueling-policy/neural-network/hidden/dense/kernel",
matrix1_qnet,
decimals=4)
test.check_var("target-policy/neural-network/hidden/dense/kernel",
matrix1_target_net)
test.check_var(
"dueling-policy/action-adapter-0/action-network/action-layer/dense/kernel",
mat_updated[1],
decimals=4)
test.check_var(
"target-policy/action-adapter-0/action-network/action-layer/dense/kernel",
matrix2_target_net)
# 6th/7th step (with exploration enabled) -> Another buffer update check.
# action: up, down (0, 2)
test.step(2, use_exploration=True)
test.check_env("state", 1)
test.check_agent(
"states_buffer", [], key_or_index="env_0"
) # <- all empty again; flushed after 6th step (when buffer was full).
test.check_agent("actions_buffer", [], key_or_index="env_0")
test.check_agent("rewards_buffer", [], key_or_index="env_0")
test.check_agent("terminals_buffer", [], key_or_index="env_0")
test.check_agent("last_memory_batch", expected_batch)
test.check_var("replay-memory/index",
1) # index has been rolled over (memory capacity is 6)
test.check_var("replay-memory/size", 6)
test.check_var(
"replay-memory/memory/states",
np.array([[1.0, 0.0, 0.0, 0.0]] * 4 + [[0.0, 0.0, 1.0, 0.0]] +
[[1.0, 0.0, 0.0, 0.0]]))
test.check_var("replay-memory/memory/actions",
np.array([2, 0, 0, 1, 2, 0]))
test.check_var("replay-memory/memory/rewards",
np.array([-1.0] * 3 + [-3.0, 1.0, -1.0]))
test.check_var("replay-memory/memory/terminals",
np.array([True, True, False, True, True, False]))
test.check_var("dueling-policy/neural-network/hidden/dense/kernel",
matrix1_qnet,
decimals=4)
test.check_var("target-policy/neural-network/hidden/dense/kernel",
matrix1_target_net)
test.check_var(
"dueling-policy/dueling-action-adapter/action-layer/dense/kernel",
matrix2_qnet,
decimals=4)
test.check_var(
"target-policy/dueling-action-adapter/action-layer/dense/kernel",
matrix2_target_net)
# 8th step -> Another buffer update check and weights update and sync.
# action: down (2)
test.step(1)
test.check_env("state", 1)
test.check_agent("states_buffer", [1], key_or_index="env_0")
test.check_agent("actions_buffer", [2], key_or_index="env_0")
test.check_agent("rewards_buffer", [-1.0], key_or_index="env_0")
test.check_agent("terminals_buffer", [False], key_or_index="env_0")
expected_batch = dict(
states=np.array([[1.0, 0.0, 0.0, 0.0], [1.0, 0.0, 0.0, 0.0]]),
actions=np.array([0, 1]),
rewards=np.array([-1.0, -3.0]),
terminals=np.array([True, True]),
next_states=np.array([[1.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0]])
# TODO: <- This is wrong and must be fixed
# (next-state of first item is from a previous insert and unrelated to first item)
)
test.check_agent("last_memory_batch", expected_batch)
test.check_var("replay-memory/index", 1)
test.check_var("replay-memory/size", 6)
test.check_var(
"replay-memory/memory/states",
np.array([[1.0, 0.0, 0.0, 0.0]] * 4 + [[0.0, 0.0, 1.0, 0.0]] +
[[1.0, 0.0, 0.0, 0.0]]))
test.check_var("replay-memory/memory/actions",
np.array([2, 0, 0, 1, 2, 0]))
test.check_var("replay-memory/memory/rewards",
np.array([-1.0, -1.0, -1.0, -3.0, 1.0, -1.0]))
test.check_var("replay-memory/memory/terminals",
np.array([True, True, False, True, True, False]))
# Assume that the sync happens first (matrices are already the same when updating).
mat_updated = self._helper_update_matrix(expected_batch, matrix1_qnet,
matrix2_qnet, matrix1_qnet,
matrix2_qnet, agent,
loss_func)
# Now target-net should be again 1 step behind policy-net.
test.check_var("dueling-policy/neural-network/hidden/dense/kernel",
mat_updated[0],
decimals=2)
test.check_var("target-policy/neural-network/hidden/dense/kernel",
matrix1_qnet,
decimals=2) # again: old matrix
test.check_var(
"dueling-policy/dueling-action-adapter/action-layer/dense/kernel",
mat_updated[1],
decimals=2)
test.check_var(
"target-policy/dueling-action-adapter/action-layer/dense/kernel",
matrix2_qnet,
decimals=2)
