class AgentA3C(Agent):
'''Agent which leverages Actor Critic Learning'''
def __init__(self,
model_path,
dtype,
seed=451):
self._seed = seed
self._idx = 0
self._dtype = dtype
self.env = LoveLetterEnv(AgentRandom(seed), seed)
state = self.env.reset()
self._model = ActorCritic(
state.shape[0], self.env.action_space).type(dtype)
self._model.load_state_dict(torch.load(model_path))
def _move(self, game):
'''Return move which ends in score hole'''
assert game.active()
self._idx += 1
state = self.env.force(game)
state = torch.from_numpy(state).type(self._dtype)
cx = Variable(torch.zeros(1, 256).type(self._dtype), volatile=True)
hx = Variable(torch.zeros(1, 256).type(self._dtype), volatile=True)
_, logit, (hx, cx) = self._model(
(Variable(state.unsqueeze(0), volatile=True), (hx, cx)))
prob = F.softmax(logit)
action_idx = prob.max(1)[1].data.cpu().numpy()[0, 0]
player_action = self.env.action_from_index(action_idx, game)
if player_action is None:
# print("ouch")
options = Agent.valid_actions(game, self._seed + self._idx)
if len(options) < 1:
raise Exception("Unable to play without actions")
random.seed(self._seed + self._idx)
return random.choice(options)
# print("playing ", self._idx, player_action)
return player_action
class TFAgent(Agent):
'''Agent which leverages tensorflow'''
def __init__(self, model_path, seed=451):
self._seed = seed
self._idx = 0
self.env = LoveLetterEnv(AgentRandom(seed), seed)
self.vec_env = DummyVecEnv([
lambda: self.env
]) # The algorithms require a vectorized environment to run
state = self.env.reset()
self._model = PPO2(MlpPolicy,
self.vec_env,
verbose=0,
tensorboard_log="./tensorboard/")
self._model.load(model_path)
def _move(self, game):
'''Return move which ends in score hole'''
assert game.active()
self._idx += 1
state = self.env.force(game)
action_idx = self._model.predict(state, deterministic=True)[0]
player_action = self.env.action_from_index(action_idx, game)
if player_action is None:
# print("ouch")
options = Agent.valid_actions(game, self._seed + self._idx)
if len(options) < 1:
raise Exception("Unable to play without actions")
random.seed(self._seed + self._idx)
return random.choice(options)
# print("playing ", self._idx, player_action)
return player_action