121 lines
5.9 KiB
Python
121 lines
5.9 KiB
Python
from copy import deepcopy
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import collections
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import numpy as np
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import torch
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from torch.distributions import Categorical
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import rltorch
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import rltorch.memory as M
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# Q-Evolutionary Policy Agent
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# Maximizes the policy with respect to the Q-Value function.
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# Since function is non-differentiabile, depends on the Evolutionary Strategy algorithm
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class QEPAgent:
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def __init__(self, policy_net, value_net, memory, config, target_value_net = None, logger = None):
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self.policy_net = policy_net
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assert isinstance(self.policy_net, rltorch.network.ESNetwork)
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self.policy_net.fitness = self.fitness
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self.value_net = value_net
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self.target_value_net = target_value_net
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self.memory = memory
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self.config = deepcopy(config)
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self.logger = logger
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self.policy_skip = 4
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def fitness(self, policy_net, value_net, state_batch):
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batch_size = len(state_batch)
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action_probabilities = policy_net(state_batch)
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action_size = action_probabilities.shape[1]
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distributions = list(map(Categorical, action_probabilities))
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actions = torch.stack([d.sample() for d in distributions])
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with torch.no_grad():
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state_values = value_net(state_batch)
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obtained_values = state_values.gather(1, actions.view(len(state_batch), 1)).squeeze(1)
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# return -obtained_values.mean().item()
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entropy_importance = 0.01 # Entropy accounting for 1% of loss seems to work well
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value_importance = 1 - entropy_importance
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# entropy_loss = (action_probabilities * torch.log2(action_probabilities)).sum(1) # Standard entropy loss from information theory
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entropy_loss = (action_probabilities - torch.tensor(1 / action_size).repeat(len(state_batch), action_size)).abs().sum(1)
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return (entropy_importance * entropy_loss - value_importance * obtained_values).mean().item()
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def learn(self, logger = None):
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if len(self.memory) < self.config['batch_size']:
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return
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if (isinstance(self.memory, M.PrioritizedReplayMemory)):
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weight_importance = self.config['prioritized_replay_weight_importance']
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# If it's a scheduler then get the next value by calling next, otherwise just use it's value
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beta = next(weight_importance) if isinstance(weight_importance, collections.Iterable) else weight_importance
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minibatch = self.memory.sample(self.config['batch_size'], beta = beta)
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state_batch, action_batch, reward_batch, next_state_batch, not_done_batch, importance_weights, batch_indexes = M.zip_batch(minibatch, priority = True)
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else:
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minibatch = self.memory.sample(self.config['batch_size'])
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state_batch, action_batch, reward_batch, next_state_batch, not_done_batch = M.zip_batch(minibatch)
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# Send to their appropriate devices
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state_batch = state_batch.to(self.value_net.device)
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action_batch = action_batch.to(self.value_net.device)
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reward_batch = reward_batch.to(self.value_net.device).float()
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next_state_batch = next_state_batch.to(self.value_net.device)
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not_done_batch = not_done_batch.to(self.value_net.device)
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state_values = self.value_net(state_batch)
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obtained_values = state_values.gather(1, action_batch.view(self.config['batch_size'], 1))
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with torch.no_grad():
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# Use the target net to produce action values for the next state
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# and the regular net to select the action
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# That way we decouple the value and action selecting processes (DOUBLE DQN)
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not_done_size = not_done_batch.sum()
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next_state_values = torch.zeros_like(state_values, device = self.value_net.device)
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if self.target_value_net is not None:
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next_state_values[not_done_batch] = self.target_value_net(next_state_batch[not_done_batch])
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next_best_action = self.value_net(next_state_batch[not_done_batch]).argmax(1)
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else:
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next_state_values[not_done_batch] = self.value_net(next_state_batch[not_done_batch])
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next_best_action = next_state_values[not_done_batch].argmax(1)
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best_next_state_value = torch.zeros(self.config['batch_size'], device = self.value_net.device)
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best_next_state_value[not_done_batch] = next_state_values[not_done_batch].gather(1, next_best_action.view((not_done_size, 1))).squeeze(1)
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expected_values = (reward_batch + (self.config['discount_rate'] * best_next_state_value)).unsqueeze(1)
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if (isinstance(self.memory, M.PrioritizedReplayMemory)):
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value_loss = (torch.as_tensor(importance_weights, device = self.value_net.device) * ((obtained_values - expected_values)**2).squeeze(1)).mean()
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else:
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value_loss = F.mse_loss(obtained_values, expected_values)
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if self.logger is not None:
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self.logger.append("Loss/Value", value_loss.item())
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self.value_net.zero_grad()
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value_loss.backward()
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self.value_net.clamp_gradients()
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self.value_net.step()
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if self.target_value_net is not None:
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if 'target_sync_tau' in self.config:
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self.target_value_net.partial_sync(self.config['target_sync_tau'])
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else:
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self.target_value_net.sync()
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if (isinstance(self.memory, M.PrioritizedReplayMemory)):
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td_error = (obtained_values - expected_values).detach().abs()
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self.memory.update_priorities(batch_indexes, td_error)
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## Policy Training
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if self.policy_skip > 0:
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self.policy_skip -= 1
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return
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self.policy_skip = 4
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if self.target_value_net is not None:
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self.policy_net.calc_gradients(self.target_value_net, state_batch)
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else:
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self.policy_net.calc_gradients(self.value_net, state_batch)
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# self.policy_net.clamp_gradients()
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self.policy_net.step()
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