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252 行
9.9 KiB
252 行
9.9 KiB
# # Unity ML-Agents Toolkit
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# ## ML-Agent Learning (PPO)
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# Contains an implementation of PPO as described in: https://arxiv.org/abs/1707.06347
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import logging
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from collections import defaultdict
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import numpy as np
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from mlagents.trainers.ppo.policy import PPOPolicy
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from mlagents.trainers.ppo.multi_gpu_policy import MultiGpuPPOPolicy, get_devices
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from mlagents.trainers.rl_trainer import RLTrainer
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from mlagents.trainers.trajectory import Trajectory
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logger = logging.getLogger("mlagents.trainers")
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class PPOTrainer(RLTrainer):
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"""The PPOTrainer is an implementation of the PPO algorithm."""
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def __init__(
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self,
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brain,
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reward_buff_cap,
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trainer_parameters,
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training,
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load,
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seed,
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run_id,
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multi_gpu,
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):
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"""
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Responsible for collecting experiences and training PPO model.
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:param trainer_parameters: The parameters for the trainer (dictionary).
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:param reward_buff_cap: Max reward history to track in the reward buffer
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:param training: Whether the trainer is set for training.
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:param load: Whether the model should be loaded.
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:param seed: The seed the model will be initialized with
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:param run_id: The identifier of the current run
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"""
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super(PPOTrainer, self).__init__(
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brain, trainer_parameters, training, run_id, reward_buff_cap
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)
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self.param_keys = [
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"batch_size",
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"beta",
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"buffer_size",
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"epsilon",
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"hidden_units",
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"lambd",
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"learning_rate",
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"max_steps",
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"normalize",
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"num_epoch",
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"num_layers",
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"time_horizon",
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"sequence_length",
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"summary_freq",
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"use_recurrent",
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"summary_path",
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"memory_size",
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"model_path",
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"reward_signals",
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]
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self.check_param_keys()
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if multi_gpu and len(get_devices()) > 1:
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self.policy = MultiGpuPPOPolicy(
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seed, brain, trainer_parameters, self.is_training, load
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)
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else:
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self.policy = PPOPolicy(
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seed, brain, trainer_parameters, self.is_training, load
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)
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for _reward_signal in self.policy.reward_signals.keys():
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self.collected_rewards[_reward_signal] = defaultdict(lambda: 0)
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def process_trajectory(self, trajectory: Trajectory) -> None:
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"""
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Takes a trajectory and processes it, putting it into the update buffer.
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Processing involves calculating value and advantage targets for model updating step.
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"""
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agent_id = trajectory.agent_id # All the agents should have the same ID
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# Add to episode_steps
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self.episode_steps[agent_id] += len(trajectory.steps)
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agent_buffer_trajectory = trajectory.to_agentbuffer()
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# Update the normalization
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if self.is_training:
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self.policy.update_normalization(agent_buffer_trajectory["vector_obs"])
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# Get all value estimates
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value_estimates = self.policy.get_batched_value_estimates(
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agent_buffer_trajectory
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)
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for name, v in value_estimates.items():
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agent_buffer_trajectory["{}_value_estimates".format(name)].extend(v)
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self.stats[self.policy.reward_signals[name].value_name].append(np.mean(v))
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value_next = self.policy.get_value_estimates(
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trajectory.next_obs,
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trajectory.done_reached and not trajectory.done_reached,
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agent_id,
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)
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# Evaluate all reward functions
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self.collected_rewards["environment"][agent_id] += np.sum(
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agent_buffer_trajectory["environment_rewards"]
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)
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for name, reward_signal in self.policy.reward_signals.items():
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evaluate_result = reward_signal.evaluate_batch(
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agent_buffer_trajectory
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).scaled_reward
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agent_buffer_trajectory["{}_rewards".format(name)].extend(evaluate_result)
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# Report the reward signals
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self.collected_rewards[name][agent_id] += np.sum(evaluate_result)
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# Compute GAE and returns
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tmp_advantages = []
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tmp_returns = []
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for name in self.policy.reward_signals:
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bootstrap_value = value_next[name]
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local_rewards = agent_buffer_trajectory[
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"{}_rewards".format(name)
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].get_batch()
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local_value_estimates = agent_buffer_trajectory[
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"{}_value_estimates".format(name)
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].get_batch()
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local_advantage = get_gae(
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rewards=local_rewards,
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value_estimates=local_value_estimates,
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value_next=bootstrap_value,
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gamma=self.policy.reward_signals[name].gamma,
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lambd=self.trainer_parameters["lambd"],
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)
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local_return = local_advantage + local_value_estimates
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# This is later use as target for the different value estimates
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agent_buffer_trajectory["{}_returns".format(name)].set(local_return)
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agent_buffer_trajectory["{}_advantage".format(name)].set(local_advantage)
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tmp_advantages.append(local_advantage)
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tmp_returns.append(local_return)
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# Get global advantages
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global_advantages = list(
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np.mean(np.array(tmp_advantages, dtype=np.float32), axis=0)
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)
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global_returns = list(np.mean(np.array(tmp_returns, dtype=np.float32), axis=0))
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agent_buffer_trajectory["advantages"].set(global_advantages)
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agent_buffer_trajectory["discounted_returns"].set(global_returns)
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# Append to update buffer
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agent_buffer_trajectory.resequence_and_append(
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self.update_buffer, training_length=self.policy.sequence_length
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)
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# If this was a terminal trajectory, append stats and reset reward collection
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if trajectory.done_reached:
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self._update_end_episode_stats(agent_id)
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def is_ready_update(self):
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"""
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Returns whether or not the trainer has enough elements to run update model
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:return: A boolean corresponding to whether or not update_model() can be run
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"""
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size_of_buffer = self.update_buffer.num_experiences
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return size_of_buffer > self.trainer_parameters["buffer_size"]
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def update_policy(self):
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"""
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Uses demonstration_buffer to update the policy.
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The reward signal generators must be updated in this method at their own pace.
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"""
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buffer_length = self.update_buffer.num_experiences
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self.trainer_metrics.start_policy_update_timer(
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number_experiences=buffer_length,
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mean_return=float(np.mean(self.cumulative_returns_since_policy_update)),
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)
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self.cumulative_returns_since_policy_update.clear()
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# Make sure batch_size is a multiple of sequence length. During training, we
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# will need to reshape the data into a batch_size x sequence_length tensor.
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batch_size = (
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self.trainer_parameters["batch_size"]
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- self.trainer_parameters["batch_size"] % self.policy.sequence_length
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)
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# Make sure there is at least one sequence
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batch_size = max(batch_size, self.policy.sequence_length)
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n_sequences = max(
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int(self.trainer_parameters["batch_size"] / self.policy.sequence_length), 1
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)
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advantages = self.update_buffer["advantages"].get_batch()
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self.update_buffer["advantages"].set(
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(advantages - advantages.mean()) / (advantages.std() + 1e-10)
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)
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num_epoch = self.trainer_parameters["num_epoch"]
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batch_update_stats = defaultdict(list)
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for _ in range(num_epoch):
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self.update_buffer.shuffle(sequence_length=self.policy.sequence_length)
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buffer = self.update_buffer
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max_num_batch = buffer_length // batch_size
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for l in range(0, max_num_batch * batch_size, batch_size):
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update_stats = self.policy.update(
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buffer.make_mini_batch(l, l + batch_size), n_sequences
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)
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for stat_name, value in update_stats.items():
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batch_update_stats[stat_name].append(value)
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for stat, stat_list in batch_update_stats.items():
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self.stats[stat].append(np.mean(stat_list))
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if self.policy.bc_module:
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update_stats = self.policy.bc_module.update()
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for stat, val in update_stats.items():
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self.stats[stat].append(val)
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self.clear_update_buffer()
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self.trainer_metrics.end_policy_update()
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def discount_rewards(r, gamma=0.99, value_next=0.0):
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"""
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Computes discounted sum of future rewards for use in updating value estimate.
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:param r: List of rewards.
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:param gamma: Discount factor.
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:param value_next: T+1 value estimate for returns calculation.
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:return: discounted sum of future rewards as list.
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"""
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discounted_r = np.zeros_like(r)
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running_add = value_next
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for t in reversed(range(0, r.size)):
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running_add = running_add * gamma + r[t]
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discounted_r[t] = running_add
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return discounted_r
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def get_gae(rewards, value_estimates, value_next=0.0, gamma=0.99, lambd=0.95):
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"""
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Computes generalized advantage estimate for use in updating policy.
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:param rewards: list of rewards for time-steps t to T.
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:param value_next: Value estimate for time-step T+1.
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:param value_estimates: list of value estimates for time-steps t to T.
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:param gamma: Discount factor.
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:param lambd: GAE weighing factor.
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:return: list of advantage estimates for time-steps t to T.
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"""
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value_estimates = np.append(value_estimates, value_next)
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delta_t = rewards + gamma * value_estimates[1:] - value_estimates[:-1]
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advantage = discount_rewards(r=delta_t, gamma=gamma * lambd)
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return advantage
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