def __init__(self, params={}):
BaseDynaAgent.__init__(self, params)
MCTSAgent.__init__(self, params)
self.episode_counter = -1
self.td_average = 0
self.average_rate = 0.1
self.mcts_count = 0
def end(self, reward):
if self.episode_counter < episodes_only_dqn:
BaseDynaAgent.end(self, reward)
elif self.episode_counter < episodes_only_dqn + episodes_only_mcts:
MCTSAgent.end(self, reward)
else:
if self.episode_counter % 2 == 0:
BaseDynaAgent.end(self, reward)
else:
MCTSAgent.end(self, reward)
def step(self, reward, observation):
if self.episode_counter < episodes_only_dqn:
action = BaseDynaAgent.step(self, reward, observation)
elif self.episode_counter < episodes_only_dqn + episodes_only_mcts:
action = MCTSAgent.step(self, reward, observation)
else:
if self.episode_counter % 2 == 0:
action = BaseDynaAgent.step(self, reward, observation)
else:
action = MCTSAgent.step(self, reward, observation)
return action
def start(self, observation):
self.episode_counter += 1
if self.episode_counter % 2 == 0:
action = BaseDynaAgent.start(self, observation)
else:
action = MCTSAgent.start(self, observation)
return action
def start(self, observation):
self.episode_counter += 1
if self.episode_counter % 2 == 0:
action = BaseDynaAgent.start(self, observation)
else:
action = MCTSAgent.start(self, observation)
self.mcts_prev_state = self.getStateRepresentation(observation)
self.mcts_prev_action = action
return action
def rollout_policy(self, state):
# random policy
# action = random.choice(self.action_list)
# DQNs policy
state = self.getStateRepresentation(state)
action_ind = BaseDynaAgent.policy(self, state)
action = self.action_list[action_ind.item()]
return action
def policy(self, state):
if self.episode_counter % 2 == 1:
action, sub_tree = None, None
for i in range(self.num_iterations):
action, sub_tree = self.MCTS_iteration()
# self.render_tree()
self.subtree_node = sub_tree
action = torch.from_numpy(np.array(
[self.getActionIndex(action)])).unsqueeze(0).to(self.device)
else:
action = BaseDynaAgent.policy(self, state)
return action
def start(self, observation):
self.episode_counter += 1
if self.keep_tree and self.root is None:
self.root = Node(None, observation)
self.expansion(self.root)
if self.keep_tree:
self.subtree_node = self.root
else:
self.subtree_node = Node(None, observation)
self.expansion(self.subtree_node)
action = BaseDynaAgent.start(self, observation)
return action
def end(self, reward):
BaseDynaAgent.end(self, reward)
def __init__(self, params={}):
BaseDynaAgent.__init__(self, params)
MCTSAgent.__init__(self, params)
self.episode_counter = -1
def __init__(self, params={}):
BaseDynaAgent.__init__(self, params)
MCTSAgent.__init__(self, params)
with open("dqn_vf_4by4.p", 'rb') as file:
self._vf = pickle.load(file)
self.episode_counter = -1
