import logging from ray.rllib.agents.trainer import with_common_config from ray.rllib.agents.dqn.dqn import GenericOffPolicyTrainer from ray.rllib.agents.ddpg.ddpg_tf_policy import DDPGTFPolicy logger = logging.getLogger(__name__) # yapf: disable # __sphinx_doc_begin__ DEFAULT_CONFIG = with_common_config({ # === Twin Delayed DDPG (TD3) and Soft Actor-Critic (SAC) tricks === # TD3: https://spinningup.openai.com/en/latest/algorithms/td3.html # In addition to settings below, you can use "exploration_noise_type" and # "exploration_gauss_act_noise" to get IID Gaussian exploration noise # instead of OU exploration noise. # twin Q-net "twin_q": False, # delayed policy update "policy_delay": 1, # target policy smoothing # (this also replaces OU exploration noise with IID Gaussian exploration # noise, for now) "smooth_target_policy": False, # gaussian stddev of target action noise for smoothing "target_noise": 0.2, # target noise limit (bound) "target_noise_clip": 0.5, # === Evaluation === # Evaluate with epsilon=0 every `evaluation_interval` training iterations. # The evaluation stats will be reported under the "evaluation" metric key. # Note that evaluation is currently not parallelized, and that for Ape-X # metrics are already only reported for the lowest epsilon workers. "evaluation_interval": None, # Number of episodes to run per evaluation period. "evaluation_num_episodes": 10, # === Model === # Apply a state preprocessor with spec given by the "model" config option # (like other RL algorithms). This is mostly useful if you have a weird # observation shape, like an image. Disabled by default. "use_state_preprocessor": False, # Postprocess the policy network model output with these hidden layers. If # use_state_preprocessor is False, then these will be the *only* hidden # layers in the network. "actor_hiddens": [400, 300], # Hidden layers activation of the postprocessing stage of the policy # network "actor_hidden_activation": "relu", # Postprocess the critic network model output with these hidden layers; # again, if use_state_preprocessor is True, then the state will be # preprocessed by the model specified with the "model" config option first. "critic_hiddens": [400, 300], # Hidden layers activation of the postprocessing state of the critic. "critic_hidden_activation": "relu", # N-step Q learning "n_step": 1, # === Exploration === "exploration_config": { # DDPG uses OrnsteinUhlenbeck (stateful) noise to be added to NN-output # actions (after a possible pure random phase of n timesteps). "type": "OrnsteinUhlenbeckNoise", # For how many timesteps should we return completely random actions, # before we start adding (scaled) noise? "random_timesteps": 1000, # The OU-base scaling factor to always apply to action-added noise. "ou_base_scale": 0.1, # The OU theta param. "ou_theta": 0.15, # The OU sigma param. "ou_sigma": 0.2, # The initial noise scaling factor. "initial_scale": 1.0, # The final noise scaling factor. "final_scale": 1.0, # Timesteps over which to anneal scale (from initial to final values). "scale_timesteps": 10000, }, # Number of env steps to optimize for before returning "timesteps_per_iteration": 1000, # Extra configuration that disables exploration. "evaluation_config": { "explore": False }, # === Replay buffer === # Size of the replay buffer. Note that if async_updates is set, then # each worker will have a replay buffer of this size. "buffer_size": 50000, # If True prioritized replay buffer will be used. "prioritized_replay": True, # Alpha parameter for prioritized replay buffer. "prioritized_replay_alpha": 0.6, # Beta parameter for sampling from prioritized replay buffer. "prioritized_replay_beta": 0.4, # Time steps over which the beta parameter is annealed. "prioritized_replay_beta_annealing_timesteps": 20000, # Final value of beta "final_prioritized_replay_beta": 0.4, # Epsilon to add to the TD errors when updating priorities. "prioritized_replay_eps": 1e-6, # Whether to LZ4 compress observations "compress_observations": False, # If set, this will fix the ratio of replayed from a buffer and learned on # timesteps to sampled from an environment and stored in the replay buffer # timesteps. Otherwise, the replay will proceed at the native ratio # determined by (train_batch_size / rollout_fragment_length). "training_intensity": None, # === Optimization === # Learning rate for the critic (Q-function) optimizer. "critic_lr": 1e-3, # Learning rate for the actor (policy) optimizer. "actor_lr": 1e-3, # Update the target network every `target_network_update_freq` steps. "target_network_update_freq": 0, # Update the target by \tau * policy + (1-\tau) * target_policy "tau": 0.002, # If True, use huber loss instead of squared loss for critic network # Conventionally, no need to clip gradients if using a huber loss "use_huber": False, # Threshold of a huber loss "huber_threshold": 1.0, # Weights for L2 regularization "l2_reg": 1e-6, # If not None, clip gradients during optimization at this value "grad_clip": None, # How many steps of the model to sample before learning starts. "learning_starts": 1500, # Update the replay buffer with this many samples at once. Note that this # setting applies per-worker if num_workers > 1. "rollout_fragment_length": 1, # Size of a batched sampled from replay buffer for training. Note that # if async_updates is set, then each worker returns gradients for a # batch of this size. "train_batch_size": 256, # === Parallelism === # Number of workers for collecting samples with. This only makes sense # to increase if your environment is particularly slow to sample, or if # you're using the Async or Ape-X optimizers. "num_workers": 0, # Whether to compute priorities on workers. "worker_side_prioritization": False, # Prevent iterations from going lower than this time span "min_iter_time_s": 1, }) # __sphinx_doc_end__ # yapf: enable def validate_config(config): if config["model"]["custom_model"]: logger.warning( "Setting use_state_preprocessor=True since a custom model " "was specified.") config["use_state_preprocessor"] = True if config["grad_clip"] is not None and config["grad_clip"] <= 0.0: raise ValueError("`grad_clip` value must be > 0.0!") if config["exploration_config"]["type"] == "ParameterNoise": if config["batch_mode"] != "complete_episodes": logger.warning( "ParameterNoise Exploration requires `batch_mode` to be " "'complete_episodes'. Setting batch_mode=complete_episodes.") config["batch_mode"] = "complete_episodes" def get_policy_class(config): if config["framework"] == "torch": from ray.rllib.agents.ddpg.ddpg_torch_policy import DDPGTorchPolicy return DDPGTorchPolicy else: return DDPGTFPolicy DDPGTrainer = GenericOffPolicyTrainer.with_updates( name="DDPG", default_config=DEFAULT_CONFIG, default_policy=DDPGTFPolicy, get_policy_class=get_policy_class, validate_config=validate_config, )