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First draft of Deep Q Learning From Demonstrations

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Brandon Rozek 2019-10-31 20:54:52 -04:00
parent 559efa38b0
commit 17391c7467
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114
rltorch/agents/DQfDAgent.py Normal file
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import collections
import rltorch.memory as M
import torch
import torch.nn.functional as F
from copy import deepcopy
import numpy as np
from pathlib import Path
from rltorch.action_selector import ArgMaxSelector
class DQfDAgent:
def __init__(self, net, imitation_net, memory, config, target_net = None, logger = None):
self.net = net
self.imitation_net = imitation_net
self.imitator = ArgMaxSelector(imitation_net, self.imitation_net.model.action_size, device = imitation_net.device)
self.target_net = target_net
self.memory = memory
self.config = deepcopy(config)
self.logger = logger
def save(self, file_location):
torch.save(self.net.model.state_dict(), file_location)
def load(self, file_location):
self.net.model.state_dict(torch.load(file_location))
self.net.model.to(self.net.device)
self.target_net.sync()
def learn(self, logger = None):
if len(self.memory) < self.config['batch_size']:
return
if (isinstance(self.memory, M.PrioritizedReplayMemory)):
weight_importance = self.config['prioritized_replay_weight_importance']
# If it's a scheduler then get the next value by calling next, otherwise just use it's value
beta = next(weight_importance) if isinstance(weight_importance, collections.Iterable) else weight_importance
minibatch = self.memory.sample(self.config['batch_size'], beta = beta)
state_batch, action_batch, reward_batch, next_state_batch, not_done_batch, importance_weights, batch_indexes = M.zip_batch(minibatch, priority = True)
else:
minibatch = self.memory.sample(self.config['batch_size'])
state_batch, action_batch, reward_batch, next_state_batch, not_done_batch = M.zip_batch(minibatch)
# Send to their appropriate devices
state_batch = state_batch.to(self.net.device)
action_batch = action_batch.to(self.net.device)
reward_batch = reward_batch.to(self.net.device).float()
next_state_batch = next_state_batch.to(self.net.device)
not_done_batch = not_done_batch.to(self.net.device)
state_values = self.net(state_batch)
obtained_values = state_values.gather(1, action_batch.view(self.config['batch_size'], 1))
with torch.no_grad():
# Use the target net to produce action values for the next state
# and the regular net to select the action
# That way we decouple the value and action selecting processes (DOUBLE DQN)
not_done_size = not_done_batch.sum()
next_state_values = torch.zeros_like(state_values, device = self.net.device)
if self.target_net is not None:
next_state_values[not_done_batch] = self.target_net(next_state_batch[not_done_batch])
next_best_action = self.net(next_state_batch[not_done_batch]).argmax(1)
else:
next_state_values[not_done_batch] = self.net(next_state_batch[not_done_batch])
next_best_action = next_state_values[not_done_batch].argmax(1)
best_next_state_value = torch.zeros(self.config['batch_size'], device = self.net.device)
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)
expected_values = (reward_batch + (self.config['discount_rate'] * best_next_state_value)).unsqueeze(1)
# If we're sampling by TD error, multiply loss by a importance weight which helps decrease overfitting
if (isinstance(self.memory, M.PrioritizedReplayMemory)):
# dqn_loss = (torch.as_tensor(importance_weights, device = self.net.device) * F.smooth_l1_loss(obtained_values, expected_values, reduction = 'none').squeeze(1)).mean()
dqn_loss = (torch.as_tensor(importance_weights, device = self.net.device) * ((obtained_values - expected_values)**2).squeeze(1)).mean()
else:
# dqn_loss = F.smooth_l1_loss(obtained_values, expected_values)
dqn_loss = F.mse_loss(obtained_values, expected_values)
# Demonstration loss
l = torch.ones_like(state_values)
expert_actions = self.imitation_net(state_batch).argmax(1)
# l(s, a) is zero for every action the expert doesn't take
for i,a in zip(range(len(state_values)), expert_actions):
l[i].fill_(0.8) # According to paper
l[i, a] = 0
if self.target_net is not None:
expert_value = self.target_net(state_batch)
else:
expert_value = self.net(state_batch)
expert_value = expert_value.gather(1, expert_actions.view((self.config['batch_size'], 1))).squeeze(1)
if isinstance(self.config['dqfd_demo_loss_weight'], collections.Iterable):
demo_importance = next(self.config['dqfd_demo_loss_weight'])
else:
demo_importance = self.config['dqfd_demo_loss_weight']
demo_loss = F.mse_loss((state_values + l).max(1)[0], expert_value)
loss = ((1 - demo_importance) * dqn_loss + demo_importance * demo_loss) / (dqn_loss + demo_loss)
if self.logger is not None:
self.logger.append("Loss", loss.item())
self.net.zero_grad()
loss.backward()
self.net.clamp_gradients()
self.net.step()
if self.target_net is not None:
if 'target_sync_tau' in self.config:
self.target_net.partial_sync(self.config['target_sync_tau'])
else:
self.target_net.sync()
# If we're sampling by TD error, readjust the weights of the experiences
if (isinstance(self.memory, M.PrioritizedReplayMemory)):
td_error = (obtained_values - expected_values).detach().abs()
self.memory.update_priorities(batch_indexes, td_error)

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rltorch/agents/__init__.py
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from .A2CSingleAgent import *
from .DQNAgent import *
from .DQfDAgent import *
from .PPOAgent import *
from .QEPAgent import *
from .REINFORCEAgent import *