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https://github.com/vale981/ray
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126 lines
4.9 KiB
Python
126 lines
4.9 KiB
Python
# Code in this file is copied and adapted from
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# https://github.com/openai/evolution-strategies-starter.
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import gym
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import numpy as np
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import ray
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import ray.experimental.tf_utils
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from ray.rllib.models import ModelCatalog
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from ray.rllib.policy.sample_batch import SampleBatch
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from ray.rllib.utils import try_import_tree
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from ray.rllib.utils.filter import get_filter
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from ray.rllib.utils.framework import try_import_tf
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from ray.rllib.utils.space_utils import get_base_struct_from_space, unbatch
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tf = try_import_tf()
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tree = try_import_tree()
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def rollout(policy, env, timestep_limit=None, add_noise=False, offset=0.0):
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"""Do a rollout.
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If add_noise is True, the rollout will take noisy actions with
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noise drawn from that stream. Otherwise, no action noise will be added.
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Args:
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policy (Policy): Rllib Policy from which to draw actions.
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env (gym.Env): Environment from which to draw rewards, done, and
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next state.
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timestep_limit (Optional[int]): Steps after which to end the rollout.
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If None, use `env.spec.max_episode_steps` or 999999.
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add_noise (bool): Indicates whether exploratory action noise should be
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added.
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offset (float): Value to subtract from the reward (e.g. survival bonus
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from humanoid).
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"""
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max_timestep_limit = 999999
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env_timestep_limit = env.spec.max_episode_steps if (
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hasattr(env, "spec") and hasattr(env.spec, "max_episode_steps")) \
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else max_timestep_limit
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timestep_limit = (env_timestep_limit if timestep_limit is None else min(
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timestep_limit, env_timestep_limit))
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rewards = []
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t = 0
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observation = env.reset()
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for _ in range(timestep_limit or max_timestep_limit):
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ac = policy.compute_actions(
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observation, add_noise=add_noise, update=True)[0]
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observation, r, done, _ = env.step(ac)
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if offset != 0.0:
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r -= np.abs(offset)
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rewards.append(r)
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t += 1
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if done:
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break
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rewards = np.array(rewards, dtype=np.float32)
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return rewards, t
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def make_session(single_threaded):
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if not single_threaded:
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return tf.Session()
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return tf.Session(
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config=tf.ConfigProto(
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inter_op_parallelism_threads=1, intra_op_parallelism_threads=1))
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class ESTFPolicy:
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def __init__(self, obs_space, action_space, config):
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self.observation_space = obs_space
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self.action_space = action_space
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self.action_space_struct = get_base_struct_from_space(action_space)
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self.action_noise_std = config["action_noise_std"]
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self.preprocessor = ModelCatalog.get_preprocessor_for_space(obs_space)
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self.observation_filter = get_filter(config["observation_filter"],
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self.preprocessor.shape)
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self.single_threaded = config.get("single_threaded", False)
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self.sess = make_session(single_threaded=self.single_threaded)
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self.inputs = tf.placeholder(tf.float32,
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[None] + list(self.preprocessor.shape))
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# Policy network.
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dist_class, dist_dim = ModelCatalog.get_action_dist(
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self.action_space, config["model"], dist_type="deterministic")
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self.model = ModelCatalog.get_model_v2(
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obs_space=self.preprocessor.observation_space,
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action_space=action_space,
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num_outputs=dist_dim,
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model_config=config["model"])
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dist_inputs, _ = self.model({SampleBatch.CUR_OBS: self.inputs})
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dist = dist_class(dist_inputs, self.model)
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self.sampler = dist.sample()
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self.variables = ray.experimental.tf_utils.TensorFlowVariables(
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dist_inputs, self.sess)
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self.num_params = sum(
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np.prod(variable.shape.as_list())
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for _, variable in self.variables.variables.items())
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self.sess.run(tf.global_variables_initializer())
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def compute_actions(self, observation, add_noise=False, update=True):
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observation = self.preprocessor.transform(observation)
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observation = self.observation_filter(observation[None], update=update)
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# `actions` is a list of (component) batches.
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actions = self.sess.run(
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self.sampler, feed_dict={self.inputs: observation})
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if add_noise:
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actions = tree.map_structure(self._add_noise, actions,
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self.action_space_struct)
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# Convert `flat_actions` to a list of lists of action components
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# (list of single actions).
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actions = unbatch(actions)
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return actions
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def _add_noise(self, single_action, single_action_space):
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if isinstance(single_action_space, gym.spaces.Box):
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single_action += np.random.randn(*single_action.shape) * \
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self.action_noise_std
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return single_action
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def set_flat_weights(self, x):
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self.variables.set_flat(x)
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def get_flat_weights(self):
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return self.variables.get_flat()
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