# Code in this file is copied and adapted from # https://github.com/openai/evolution-strategies-starter. from collections import namedtuple import logging import numpy as np import random import time import ray from ray.rllib.agents import Trainer, with_common_config from ray.rllib.agents.es import optimizers, utils from ray.rllib.agents.es.es_tf_policy import ESTFPolicy, rollout from ray.rllib.env.env_context import EnvContext from ray.rllib.policy.sample_batch import DEFAULT_POLICY_ID from ray.rllib.utils import FilterManager from ray.rllib.utils.annotations import override from ray.rllib.utils.deprecation import Deprecated from ray.rllib.utils.torch_utils import set_torch_seed from ray.rllib.utils.typing import TrainerConfigDict logger = logging.getLogger(__name__) Result = namedtuple("Result", [ "noise_indices", "noisy_returns", "sign_noisy_returns", "noisy_lengths", "eval_returns", "eval_lengths" ]) # yapf: disable # __sphinx_doc_begin__ DEFAULT_CONFIG = with_common_config({ "action_noise_std": 0.01, "l2_coeff": 0.005, "noise_stdev": 0.02, "episodes_per_batch": 1000, "train_batch_size": 10000, "eval_prob": 0.003, "return_proc_mode": "centered_rank", "num_workers": 10, "stepsize": 0.01, "observation_filter": "MeanStdFilter", "noise_size": 250000000, "report_length": 10, # ARS will use Trainer's evaluation WorkerSet (if evaluation_interval > 0). # Therefore, we must be careful not to use more than 1 env per eval worker # (would break ESPolicy's compute_single_action method) and to not do # obs-filtering. "evaluation_config": { "num_envs_per_worker": 1, "observation_filter": "NoFilter" }, }) # __sphinx_doc_end__ # yapf: enable @ray.remote def create_shared_noise(count): """Create a large array of noise to be shared by all workers.""" seed = 123 noise = np.random.RandomState(seed).randn(count).astype(np.float32) return noise class SharedNoiseTable: def __init__(self, noise): self.noise = noise assert self.noise.dtype == np.float32 def get(self, i, dim): return self.noise[i:i + dim] def sample_index(self, dim): return np.random.randint(0, len(self.noise) - dim + 1) @ray.remote class Worker: def __init__(self, config, policy_params, env_creator, noise, worker_index, min_task_runtime=0.2): # Set Python random, numpy, env, and torch/tf seeds. seed = config.get("seed") if seed is not None: # Python random module. random.seed(seed) # Numpy. np.random.seed(seed) # Torch. if config.get("framework") == "torch": set_torch_seed(seed) self.min_task_runtime = min_task_runtime self.config = config self.config.update(policy_params) self.config["single_threaded"] = True self.noise = SharedNoiseTable(noise) env_context = EnvContext(config["env_config"] or {}, worker_index) self.env = env_creator(env_context) # Seed the env, if gym.Env. if not hasattr(self.env, "seed"): logger.info("Env doesn't support env.seed(): {}".format(self.env)) # Gym.env. else: self.env.seed(seed) from ray.rllib import models self.preprocessor = models.ModelCatalog.get_preprocessor( self.env, config["model"]) _policy_class = get_policy_class(config) self.policy = _policy_class(self.env.observation_space, self.env.action_space, config) @property def filters(self): return {DEFAULT_POLICY_ID: self.policy.observation_filter} def sync_filters(self, new_filters): for k in self.filters: self.filters[k].sync(new_filters[k]) def get_filters(self, flush_after=False): return_filters = {} for k, f in self.filters.items(): return_filters[k] = f.as_serializable() if flush_after: f.clear_buffer() return return_filters def rollout(self, timestep_limit, add_noise=True): rollout_rewards, rollout_fragment_length = rollout( self.policy, self.env, timestep_limit=timestep_limit, add_noise=add_noise) return rollout_rewards, rollout_fragment_length def do_rollouts(self, params, timestep_limit=None): # Set the network weights. self.policy.set_flat_weights(params) noise_indices, returns, sign_returns, lengths = [], [], [], [] eval_returns, eval_lengths = [], [] # Perform some rollouts with noise. task_tstart = time.time() while (len(noise_indices) == 0 or time.time() - task_tstart < self.min_task_runtime): if np.random.uniform() < self.config["eval_prob"]: # Do an evaluation run with no perturbation. self.policy.set_flat_weights(params) rewards, length = self.rollout(timestep_limit, add_noise=False) eval_returns.append(rewards.sum()) eval_lengths.append(length) else: # Do a regular run with parameter perturbations. noise_index = self.noise.sample_index(self.policy.num_params) perturbation = self.config["noise_stdev"] * self.noise.get( noise_index, self.policy.num_params) # These two sampling steps could be done in parallel on # different actors letting us update twice as frequently. self.policy.set_flat_weights(params + perturbation) rewards_pos, lengths_pos = self.rollout(timestep_limit) self.policy.set_flat_weights(params - perturbation) rewards_neg, lengths_neg = self.rollout(timestep_limit) noise_indices.append(noise_index) returns.append([rewards_pos.sum(), rewards_neg.sum()]) sign_returns.append( [np.sign(rewards_pos).sum(), np.sign(rewards_neg).sum()]) lengths.append([lengths_pos, lengths_neg]) return Result( noise_indices=noise_indices, noisy_returns=returns, sign_noisy_returns=sign_returns, noisy_lengths=lengths, eval_returns=eval_returns, eval_lengths=eval_lengths) def get_policy_class(config): if config["framework"] == "torch": from ray.rllib.agents.es.es_torch_policy import ESTorchPolicy policy_cls = ESTorchPolicy else: policy_cls = ESTFPolicy return policy_cls class ESTrainer(Trainer): """Large-scale implementation of Evolution Strategies in Ray.""" @classmethod @override(Trainer) def get_default_config(cls) -> TrainerConfigDict: return DEFAULT_CONFIG @override(Trainer) def validate_config(self, config: TrainerConfigDict) -> None: # Call super's validation method. super().validate_config(config) if config["num_gpus"] > 1: raise ValueError("`num_gpus` > 1 not yet supported for ES!") if config["num_workers"] <= 0: raise ValueError("`num_workers` must be > 0 for ES!") if config["evaluation_config"]["num_envs_per_worker"] != 1: raise ValueError( "`evaluation_config.num_envs_per_worker` must always be 1 for " "ES! To parallelize evaluation, increase " "`evaluation_num_workers` to > 1.") if config["evaluation_config"]["observation_filter"] != "NoFilter": raise ValueError( "`evaluation_config.observation_filter` must always be " "`NoFilter` for ES!") @override(Trainer) def _init(self, config, env_creator): self.validate_config(config) env_context = EnvContext(config["env_config"] or {}, worker_index=0) env = env_creator(env_context) self._policy_class = get_policy_class(config) self.policy = self._policy_class( obs_space=env.observation_space, action_space=env.action_space, config=config) self.optimizer = optimizers.Adam(self.policy, config["stepsize"]) self.report_length = config["report_length"] # Create the shared noise table. logger.info("Creating shared noise table.") noise_id = create_shared_noise.remote(config["noise_size"]) self.noise = SharedNoiseTable(ray.get(noise_id)) # Create the actors. logger.info("Creating actors.") self._workers = [ Worker.remote(config, {}, env_creator, noise_id, idx + 1) for idx in range(config["num_workers"]) ] self.episodes_so_far = 0 self.reward_list = [] self.tstart = time.time() @override(Trainer) def get_policy(self, policy=DEFAULT_POLICY_ID): if policy != DEFAULT_POLICY_ID: raise ValueError("ES has no policy '{}'! Use {} " "instead.".format(policy, DEFAULT_POLICY_ID)) return self.policy @override(Trainer) def step_attempt(self): config = self.config theta = self.policy.get_flat_weights() assert theta.dtype == np.float32 assert len(theta.shape) == 1 # Put the current policy weights in the object store. theta_id = ray.put(theta) # Use the actors to do rollouts, note that we pass in the ID of the # policy weights. results, num_episodes, num_timesteps = self._collect_results( theta_id, config["episodes_per_batch"], config["train_batch_size"]) all_noise_indices = [] all_training_returns = [] all_training_lengths = [] all_eval_returns = [] all_eval_lengths = [] # Loop over the results. for result in results: all_eval_returns += result.eval_returns all_eval_lengths += result.eval_lengths all_noise_indices += result.noise_indices all_training_returns += result.noisy_returns all_training_lengths += result.noisy_lengths assert len(all_eval_returns) == len(all_eval_lengths) assert (len(all_noise_indices) == len(all_training_returns) == len(all_training_lengths)) self.episodes_so_far += num_episodes # Assemble the results. eval_returns = np.array(all_eval_returns) eval_lengths = np.array(all_eval_lengths) noise_indices = np.array(all_noise_indices) noisy_returns = np.array(all_training_returns) noisy_lengths = np.array(all_training_lengths) # Process the returns. if config["return_proc_mode"] == "centered_rank": proc_noisy_returns = utils.compute_centered_ranks(noisy_returns) else: raise NotImplementedError(config["return_proc_mode"]) # Compute and take a step. g, count = utils.batched_weighted_sum( proc_noisy_returns[:, 0] - proc_noisy_returns[:, 1], (self.noise.get(index, self.policy.num_params) for index in noise_indices), batch_size=500) g /= noisy_returns.size assert (g.shape == (self.policy.num_params, ) and g.dtype == np.float32 and count == len(noise_indices)) # Compute the new weights theta. theta, update_ratio = self.optimizer.update(-g + config["l2_coeff"] * theta) # Set the new weights in the local copy of the policy. self.policy.set_flat_weights(theta) # Store the rewards if len(all_eval_returns) > 0: self.reward_list.append(np.mean(eval_returns)) # Now sync the filters FilterManager.synchronize({ DEFAULT_POLICY_ID: self.policy.observation_filter }, self._workers) info = { "weights_norm": np.square(theta).sum(), "grad_norm": np.square(g).sum(), "update_ratio": update_ratio, "episodes_this_iter": noisy_lengths.size, "episodes_so_far": self.episodes_so_far, } reward_mean = np.mean(self.reward_list[-self.report_length:]) result = dict( episode_reward_mean=reward_mean, episode_len_mean=eval_lengths.mean(), timesteps_this_iter=noisy_lengths.sum(), info=info) return result @override(Trainer) def compute_single_action(self, observation, *args, **kwargs): action, _, _ = self.policy.compute_actions([observation], update=False) if kwargs.get("full_fetch"): return action[0], [], {} return action[0] @Deprecated(new="compute_single_action", error=False) def compute_action(self, observation, *args, **kwargs): return self.compute_single_action(observation, *args, **kwargs) @override(Trainer) def _sync_weights_to_workers(self, *, worker_set=None, workers=None): # Broadcast the new policy weights to all evaluation workers. assert worker_set is not None logger.info("Synchronizing weights to evaluation workers.") weights = ray.put(self.policy.get_flat_weights()) worker_set.foreach_policy( lambda p, pid: p.set_flat_weights(ray.get(weights))) @override(Trainer) def cleanup(self): # workaround for https://github.com/ray-project/ray/issues/1516 for w in self._workers: w.__ray_terminate__.remote() def _collect_results(self, theta_id, min_episodes, min_timesteps): num_episodes, num_timesteps = 0, 0 results = [] while num_episodes < min_episodes or num_timesteps < min_timesteps: logger.info( "Collected {} episodes {} timesteps so far this iter".format( num_episodes, num_timesteps)) rollout_ids = [ worker.do_rollouts.remote(theta_id) for worker in self._workers ] # Get the results of the rollouts. for result in ray.get(rollout_ids): results.append(result) # Update the number of episodes and the number of timesteps # keeping in mind that result.noisy_lengths is a list of lists, # where the inner lists have length 2. num_episodes += sum(len(pair) for pair in result.noisy_lengths) num_timesteps += sum( sum(pair) for pair in result.noisy_lengths) return results, num_episodes, num_timesteps def __getstate__(self): return { "weights": self.policy.get_flat_weights(), "filter": self.policy.observation_filter, "episodes_so_far": self.episodes_so_far, } def __setstate__(self, state): self.episodes_so_far = state["episodes_so_far"] self.policy.set_flat_weights(state["weights"]) self.policy.observation_filter = state["filter"] FilterManager.synchronize({ DEFAULT_POLICY_ID: self.policy.observation_filter }, self._workers)