def __init__(self, env_name, config, upload_dir=None):
config.update({"alg": "EvolutionStrategies"})
Algorithm.__init__(self, env_name, config, upload_dir=upload_dir)
policy_params = {
"ac_noise_std": 0.01
}
env = gym.make(env_name)
utils.make_session(single_threaded=False)
self.policy = policies.GenericPolicy(
env.observation_space, env.action_space, **policy_params)
tf_util.initialize()
self.optimizer = optimizers.Adam(self.policy, config["stepsize"])
self.ob_stat = utils.RunningStat(env.observation_space.shape, eps=1e-2)
# Create the shared noise table.
print("Creating shared noise table.")
noise_id = create_shared_noise.remote()
self.noise = SharedNoiseTable(ray.get(noise_id))
# Create the actors.
print("Creating actors.")
self.workers = [
Worker.remote(config, policy_params, env_name, noise_id)
for _ in range(config["num_workers"])]
self.episodes_so_far = 0
self.timesteps_so_far = 0
self.tstart = time.time()
self.iteration = 0
def __init__(self, env_name, config, upload_dir=None):
config.update({"alg": "PolicyGradient"})
Algorithm.__init__(self, env_name, config, upload_dir=upload_dir)
# TODO(ekl): preprocessor should be associated with the env elsewhere
if self.env_name == "Pong-v0":
preprocessor = AtariPixelPreprocessor()
elif self.env_name == "Pong-ram-v3":
preprocessor = AtariRamPreprocessor()
elif self.env_name == "CartPole-v0" or self.env_name == "CartPole-v1":
preprocessor = NoPreprocessor()
elif self.env_name == "Hopper-v1":
preprocessor = NoPreprocessor()
elif self.env_name == "Walker2d-v1":
preprocessor = NoPreprocessor()
elif self.env_name == "Humanoid-v1":
preprocessor = NoPreprocessor()
else:
preprocessor = AtariPixelPreprocessor()
self.preprocessor = preprocessor
self.global_step = 0
self.j = 0
self.kl_coeff = config["kl_coeff"]
self.model = Agent(self.env_name, 1, self.preprocessor, self.config,
self.logdir, False)
self.agents = [
RemoteAgent.remote(self.env_name, 1, self.preprocessor,
self.config, self.logdir, True)
for _ in range(config["num_agents"])
]
self.start_time = time.time()
def __init__(self, env_name, config):
Algorithm.__init__(self, env_name, config)
# TODO(ekl) the preprocessor should be associated with the env elsewhere
if self.env_name == "Pong-v0":
preprocessor = AtariPixelPreprocessor()
elif self.env_name == "Pong-ram-v3":
preprocessor = AtariRamPreprocessor()
elif self.env_name == "CartPole-v0":
preprocessor = NoPreprocessor()
elif self.env_name == "Walker2d-v1":
preprocessor = NoPreprocessor()
else:
preprocessor = AtariPixelPreprocessor()
self.preprocessor = preprocessor
self.global_step = 0
self.j = 0
self.kl_coeff = config["kl_coeff"]
self.model = Agent(
self.env_name, 1, self.preprocessor, self.config, False)
self.agents = [
RemoteAgent.remote(
self.env_name, 1, self.preprocessor, self.config, True)
for _ in range(config["num_agents"])]
def __init__(self, env_name, config):
Algorithm.__init__(self, env_name, config)
policy_params = {
"ac_bins": "continuous:",
"ac_noise_std": 0.01,
"nonlin_type": "tanh",
"hidden_dims": [256, 256],
"connection_type": "ff"
}
# Create the shared noise table.
print("Creating shared noise table.")
noise_id = create_shared_noise.remote()
self.noise = SharedNoiseTable(ray.get(noise_id))
# Create the actors.
print("Creating actors.")
self.workers = [
Worker.remote(config, policy_params, env_name, noise_id)
for _ in range(config.num_workers)
]
env = gym.make(env_name)
utils.make_session(single_threaded=False)
self.policy = policies.MujocoPolicy(env.observation_space,
env.action_space, **policy_params)
tf_util.initialize()
self.optimizer = optimizers.Adam(self.policy, config.stepsize)
self.ob_stat = utils.RunningStat(env.observation_space.shape, eps=1e-2)
self.episodes_so_far = 0
self.timesteps_so_far = 0
self.tstart = time.time()
self.iteration = 0
def __init__(self, env_name, config, upload_dir=None):
config.update({"alg": "DQN"})
Algorithm.__init__(self, env_name, config, upload_dir=upload_dir)
env = gym.make(env_name)
env = ScaledFloatFrame(wrap_dqn(env))
self.env = env
model = models.cnn_to_mlp(convs=[(32, 8, 4), (64, 4, 2), (64, 3, 1)],
hiddens=[256],
dueling=True)
sess = U.make_session(num_cpu=config["num_cpu"])
sess.__enter__()
def make_obs_ph(name):
return U.BatchInput(env.observation_space.shape, name=name)
self.act, self.optimize, self.update_target, self.debug = build_train(
make_obs_ph=make_obs_ph,
q_func=model,
num_actions=env.action_space.n,
optimizer=tf.train.AdamOptimizer(learning_rate=config["lr"]),
gamma=config["gamma"],
grad_norm_clipping=10)
# Create the replay buffer
if config["prioritized_replay"]:
self.replay_buffer = PrioritizedReplayBuffer(
config["buffer_size"],
alpha=config["prioritized_replay_alpha"])
prioritized_replay_beta_iters = (
config["prioritized_replay_beta_iters"])
if prioritized_replay_beta_iters is None:
prioritized_replay_beta_iters = (
config["schedule_max_timesteps"])
self.beta_schedule = LinearSchedule(
prioritized_replay_beta_iters,
initial_p=config["prioritized_replay_beta0"],
final_p=1.0)
else:
self.replay_buffer = ReplayBuffer(config["buffer_size"])
self.beta_schedule = None
# Create the schedule for exploration starting from 1.
self.exploration = LinearSchedule(
schedule_timesteps=int(config["exploration_fraction"] *
config["schedule_max_timesteps"]),
initial_p=1.0,
final_p=config["exploration_final_eps"])
# Initialize the parameters and copy them to the target network.
U.initialize()
self.update_target()
self.episode_rewards = [0.0]
self.episode_lengths = [0.0]
self.saved_mean_reward = None
self.obs = self.env.reset()
self.num_timesteps = 0
self.num_iterations = 0
def __init__(self, env_name, config):
Algorithm.__init__(self, env_name, config)
self.env = create_env(env_name)
self.policy = LSTMPolicy(self.env.observation_space.shape,
self.env.action_space.n, 0)
self.agents = [
Runner.remote(env_name, i) for i in range(config["num_workers"])
]
self.parameters = self.policy.get_weights()
self.iteration = 0
def __init__(self, env_name, config, upload_dir=None):
config.update({"alg": "A3C"})
Algorithm.__init__(self, env_name, config, upload_dir=upload_dir)
self.env = create_env(env_name)
self.policy = LSTMPolicy(self.env.observation_space.shape,
self.env.action_space.n, 0)
self.agents = [
Runner.remote(env_name, i, self.logdir)
for i in range(config["num_workers"])
]
self.parameters = self.policy.get_weights()
self.iteration = 0
def __init__(self, env_name, config, upload_dir=None):
config.update({"alg": "DQN"})
Algorithm.__init__(self, env_name, config, upload_dir=upload_dir)
env = gym.make(env_name)
# TODO(ekl): replace this with RLlib preprocessors
if "NoFrameskip" in env_name:
env = ScaledFloatFrame(wrap_dqn(env))
self.env = env
num_cpu = config["num_cpu"]
tf_config = tf.ConfigProto(inter_op_parallelism_threads=num_cpu,
intra_op_parallelism_threads=num_cpu)
self.sess = tf.Session(config=tf_config)
self.dqn_graph = models.DQNGraph(env, config)
# Create the replay buffer
if config["prioritized_replay"]:
self.replay_buffer = PrioritizedReplayBuffer(
config["buffer_size"],
alpha=config["prioritized_replay_alpha"])
prioritized_replay_beta_iters = (
config["prioritized_replay_beta_iters"])
if prioritized_replay_beta_iters is None:
prioritized_replay_beta_iters = (
config["schedule_max_timesteps"])
self.beta_schedule = LinearSchedule(
prioritized_replay_beta_iters,
initial_p=config["prioritized_replay_beta0"],
final_p=1.0)
else:
self.replay_buffer = ReplayBuffer(config["buffer_size"])
self.beta_schedule = None
# Create the schedule for exploration starting from 1.
self.exploration = LinearSchedule(
schedule_timesteps=int(config["exploration_fraction"] *
config["schedule_max_timesteps"]),
initial_p=1.0,
final_p=config["exploration_final_eps"])
# Initialize the parameters and copy them to the target network.
self.sess.run(tf.global_variables_initializer())
self.dqn_graph.update_target(self.sess)
self.episode_rewards = [0.0]
self.episode_lengths = [0.0]
self.saved_mean_reward = None
self.obs = self.env.reset()
self.num_timesteps = 0
self.num_iterations = 0
self.file_writer = tf.summary.FileWriter(self.logdir, self.sess.graph)
def __init__(self, env_name, config, upload_dir=None):
config.update({"alg": "PolicyGradient"})
Algorithm.__init__(self, env_name, config, upload_dir=upload_dir)
self.global_step = 0
self.j = 0
self.kl_coeff = config["kl_coeff"]
self.model = Agent(self.env_name, 1, self.config, self.logdir, False)
self.agents = [
RemoteAgent.remote(
self.env_name, 1, self.config, self.logdir, True)
for _ in range(config["num_agents"])]
self.start_time = time.time()
def __init__(self, env_name, config, upload_dir=None):
config.update({"alg": "PolicyGradient"})
Algorithm.__init__(self, env_name, config, upload_dir=upload_dir)
self.global_step = 0
self.j = 0
self.kl_coeff = config["kl_coeff"]
self.model = Agent(self.env_name, 1, self.config, self.logdir, False)
self.agents = [
RemoteAgent.remote(
self.env_name, 1, self.config, self.logdir, True)
for _ in range(config["num_agents"])]
self.start_time = time.time()
# TF does not support to write logs to S3 at the moment
write_tf_logs = config["write_logs"] and self.logdir.startswith("file")
if write_tf_logs:
self.file_writer = tf.summary.FileWriter(
self.logdir, self.model.sess.graph)
else:
self.file_writer = None
self.saver = tf.train.Saver(max_to_keep=None)
