class DQNTester:
def __init__(self, skip_frame, num_actions, load, rand_val=0.05):
rand_vals = (rand_val, rand_val, 2)
self.action_handler = ActionHandler(rand_vals)
self.cnn = CNN((None, skip_frame, 84, 84), num_actions, 1)
self.cnn.load(load)
self.q_vals = list()
self.skip_frame = skip_frame
self.exp_handler = DataSet(84,
84,
np.random.RandomState(),
phi_length=skip_frame)
self.skip_frame = skip_frame
self.state_tm1 = np.zeros((84, 84), dtype=np.uint8)
def get_game_action(self):
q_vals = self.cnn.get_output(
self.exp_handler.phi(self.state_tm1).reshape(
1, self.skip_frame, 84, 84))[0]
self.q_vals.append(q_vals)
return self.action_handler.action_vect_to_game_action(q_vals)
def frames_processed(self, frames, action_performed, reward):
game_action = self.action_handler.game_action_to_action_ind(
action_performed)
self.exp_handler.add_sample(self.state_tm1, game_action, reward, False)
self.state_tm1 = frames[-1]
def set_legal_actions(self, legal_actions):
self.action_handler.set_legal_actions(legal_actions)
class DQNLearner(learner):
def __init__(self, skip_frame, num_actions, load=None, random_state=np.random.RandomState()):
super().__init__()
rand_vals = (1, 0.1, 1000000) # starting at 1 anneal eGreedy policy to 0.1 over 1,000,000 actions
self.action_handler = ActionHandler(ActionPolicy.eGreedy, rand_vals)
self.minimum_replay_size = 100
self.exp_handler = DataSet(84, 84, random_state, max_steps=1000000, phi_length=skip_frame)
self.cnn = CNN((None, skip_frame, 84, 84), num_actions)
self.skip_frame = skip_frame
self.discount = .95
self.costList = list()
self.state_tm1 = None
if load is not None:
self.cnn.load(load)
def frames_processed(self, frames, action_performed, reward):
game_action = self.action_handler.game_action_to_action_ind(action_performed)
if self.state_tm1 is not None:
self.exp_handler.add_sample(self.state_tm1, game_action, reward, False)
# generate minibatch data
if self.exp_handler.size > self.minimum_replay_size:
states, actions, rewards, state_tp1s, terminal = self.exp_handler.random_batch(32)
cost = self.cnn.train(states, actions, rewards, state_tp1s, terminal)
self.costList.append(cost)
self.action_handler.anneal()
self.state_tm1 = frames[-1]
def get_action(self, processed_screens):
return self.cnn.get_output(processed_screens)[0]
def game_over(self):
self.exp_handler.add_terminal() # adds a terminal
def get_game_action(self):
return self.action_handler.action_vect_to_game_action(
self.get_action(self.exp_handler.phi(self.state_tm1).reshape(1, self.skip_frame, 84, 84)))
def set_legal_actions(self, legal_actions):
self.action_handler.set_legal_actions(legal_actions)
def save(self, file):
self.cnn.save(file)
def get_cost_list(self):
return self.costList
class PrioritizedExperienceLearner(learner):
def __init__(self, skip_frame, num_actions, load=None):
super().__init__()
rand_vals = (1, 0.1, 10000 / skip_frame) # starting at 1 anneal eGreedy policy to 0.1 over 1,000,000/skip_frame
self.action_handler = ActionHandler(ActionPolicy.eGreedy, rand_vals)
self.exp_handler = PrioritizedExperienceHandler(1000000 / skip_frame)
self.train_handler = TrainHandler(32, num_actions)
self.cnn = CNN((None, skip_frame, 86, 80), num_actions, 0.1)
self.discount = 0.99
if load is not None:
self.cnn.load(load)
def frames_processed(self, frames, action_performed, reward):
self.exp_handler.add_experience(frames, self.action_handler.game_action_to_action_ind(action_performed), reward)
self.train_handler.train_prioritized(self.exp_handler, 0.99, self.cnn)
self.action_handler.anneal()
def plot_tree(self):
self.exp_handler.tree.plot()
def get_action(self, game_input):
return self.cnn.get_output(game_input)[0]
def game_over(self):
self.exp_handler.trim() # trim experience replay of learner
self.exp_handler.add_terminal() # adds a terminal
def get_game_action(self, game_input):
return self.action_handler.action_vect_to_game_action(self.get_action(game_input))
def set_legal_actions(self, legal_actions):
self.action_handler.set_legal_actions(legal_actions)
def save(self, file):
self.cnn.save(file)
def get_cost_list(self):
return self.train_handler.costList
class DQNTester:
def __init__(self, skip_frame, num_actions, load, rand_val=0.05):
rand_vals = (rand_val, rand_val, 2)
self.action_handler = ActionHandler(ActionPolicy.eGreedy, rand_vals)
self.cnn = CNN((None, skip_frame, 84, 84), num_actions, 1)
self.cnn.load(load)
self.q_vals = list()
self.skip_frame = skip_frame
self.exp_handler = DataSet(84, 84, np.random.RandomState(), phi_length=skip_frame)
self.skip_frame = skip_frame
self.state_tm1 = np.zeros((84, 84), dtype=np.uint8)
def get_game_action(self):
q_vals = self.cnn.get_output(self.exp_handler.phi(self.state_tm1).reshape(1, self.skip_frame, 84, 84))[0]
self.q_vals.append(q_vals)
return self.action_handler.action_vect_to_game_action(q_vals)
def frames_processed(self, frames, action_performed, reward):
game_action = self.action_handler.game_action_to_action_ind(action_performed)
self.exp_handler.add_sample(self.state_tm1, game_action, reward, False)
self.state_tm1 = frames[-1]
def set_legal_actions(self, legal_actions):
self.action_handler.set_legal_actions(legal_actions)
class DQNLearner(learner):
def __init__(self,
skip_frame,
num_actions,
load=None,
random_state=np.random.RandomState()):
super().__init__()
rand_vals = (
1, 0.1, 1000000
) # starting at 1 anneal eGreedy policy to 0.1 over 1,000,000 actions
self.action_handler = ActionHandler(rand_vals)
self.minimum_replay_size = 100
self.exp_handler = DataSet(84,
84,
random_state,
max_steps=1000000,
phi_length=skip_frame)
self.cnn = CNN((None, skip_frame, 84, 84), num_actions)
self.skip_frame = skip_frame
self.discount = .95
self.costList = list()
self.state_tm1 = None
if load is not None:
self.cnn.load(load)
def frames_processed(self, frames, action_performed, reward):
game_action = self.action_handler.game_action_to_action_ind(
action_performed)
if self.state_tm1 is not None:
self.exp_handler.add_sample(self.state_tm1, game_action, reward,
False)
# generate minibatch data
if self.exp_handler.size > self.minimum_replay_size:
states, actions, rewards, state_tp1s, terminal = self.exp_handler.random_batch(
32)
cost = self.cnn.train(states, actions, rewards, state_tp1s,
terminal)
self.costList.append(cost)
self.action_handler.anneal()
self.state_tm1 = frames[-1]
def get_action(self, processed_screens):
return self.cnn.get_output(processed_screens)[0]
def game_over(self):
self.exp_handler.add_terminal() # adds a terminal
def get_game_action(self):
return self.action_handler.action_vect_to_game_action(
self.get_action(
self.exp_handler.phi(self.state_tm1).reshape(
1, self.skip_frame, 84, 84)))
def set_legal_actions(self, legal_actions):
self.action_handler.set_legal_actions(legal_actions)
def save(self, file):
self.cnn.save(file)
def get_cost_list(self):
return self.costList
