brozek/rltorch
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity

Playing around with QEP

Browse source
This commit is contained in:
Brandon Rozek 2019-03-14 00:53:51 -04:00
parent 8683b75ad9
commit cdfd3ab6b9
1 changed files with 13 additions and 6 deletions
Download patch file Download diff file Expand all files Collapse all files

19
rltorch/agents/QEPAgent.py
View file

@ -1,5 +1,6 @@
from copy import deepcopy
import collections
import numpy as np
import torch
from torch.distributions import Categorical
import rltorch
@ -18,21 +19,27 @@ class QEPAgent:
self.memory = memory
self.config = deepcopy(config)
self.logger = logger
self.policy_skip = 10
self.policy_skip = 4
def fitness(self, policy_net, value_net, state_batch):
batch_size = len(state_batch)
action_probabilities = policy_net(state_batch)
action_size = action_probabilities.shape[1]
distributions = list(map(Categorical, action_probabilities))
actions = torch.stack([d.sample() for d in distributions])
with torch.no_grad():
state_values = value_net(state_batch)
obtained_values = state_values.gather(1, actions.view(len(state_batch), 1)).squeeze(1)
# return -obtained_values.mean().item()
entropy_importance = 0.01 # Entropy accounting for 1% of loss seems to work well
entropy_loss = (action_probabilities * torch.log(action_probabilities)).sum(1)
return (entropy_importance * entropy_loss - (1 - entropy_importance) * obtained_values).mean().item()
value_importance = 1 - entropy_importance
# entropy_loss = (action_probabilities * torch.log2(action_probabilities)).sum(1) # Standard entropy loss from information theory
entropy_loss = (action_probabilities - torch.tensor(1 / action_size).repeat(len(state_batch), action_size)).abs().sum(1)
return (entropy_importance * entropy_loss - value_importance * obtained_values).mean().item()
def learn(self, logger = None):
@ -75,7 +82,7 @@ class QEPAgent:
best_next_state_value = torch.zeros(self.config['batch_size'], device = self.value_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)
expected_values = (reward_batch.float() + (self.config['discount_rate'] * best_next_state_value)).unsqueeze(1)
if (isinstance(self.memory, M.PrioritizedReplayMemory)):
value_loss = (torch.as_tensor(importance_weights, device = self.value_net.device) * ((obtained_values - expected_values)**2).squeeze(1)).mean()
@ -104,7 +111,7 @@ class QEPAgent:
if self.policy_skip > 0:
self.policy_skip -= 1
return
self.policy_skip = 10
self.policy_skip = 4
if self.target_value_net is not None:
self.policy_net.calc_gradients(self.target_value_net, state_batch)
else: