def __init__(self,
env = ProcessedAtariEnv(),
memory = PrioritizedExperienceReplay(),
policy_network = None,
target_network = None,
frame_shape = (84, 84),
save_path = None,
logger = QLearningLogger):
self.env = env
self.memory = memory
self.policy_network = policy_network
self.target_network = target_network
self.num_stacked_frames = memory.num_stacked_frames
self.save_path = None
if save_path is not None:
self.save_path = save_path
self.logger = logger(self.save_path)
# internal variables
self._current_state = np.zeros((1, self.num_stacked_frames, *frame_shape), dtype = np.uint8)
self._step_counter = 0
# logging variables
self._q_values = []
self._losses = []
self._score = 0.0
def __init__(self,
env = ProcessedAtariEnv(),
memory = PrioritizedExperienceReplay(),
policy_network = None,#DeepQNetwork(),
target_network = None,#DeepQNetwork(),
frame_shape = (84, 84),
save_path = None,
num_actions = 4,
epsilon = 0.05):
BaseQAgent.__init__(self, env, memory, policy_network, target_network, frame_shape, save_path)
self.epsilon = epsilon
self.num_actions = num_actions
def __init__(self,
env = ProcessedAtariEnv(),
memory = PrioritizedExperienceReplay(),
policy_network = None,#DeepQNetwork(),
target_network = None,#DeepQNetwork(),
frame_shape = (84, 84),
save_path = None,
num_actions = 4,
eps_schedule = [[1.0, 0.1, 1000000],
[0.1, 0.001, 5000000]]):
BaseQAgent.__init__(self, env, memory, policy_network, target_network, frame_shape, save_path)
self.eps_schedule = np.array(eps_schedule)
self.eps_schedule[:,2] = np.cumsum(self.eps_schedule[:,2])
self.eps_lag = 0
self.num_actions = num_actions
def __init__(self,
env = ProcessedAtariEnv(),
memory = PrioritizedExperienceReplay(),
policy_network = None,#DeepQNetwork(),
target_network = None, #DeepQNetwork(),
frame_shape = (84, 84),
save_path = None,
discount_factor = 0.99,
n_step = None,
double_q = False,
expert_memory = None,
prioritized_replay = False,
num_actions = 4,
epsilon = 0.05):
EpsilonGreedyAgent.__init__(self, env, memory, policy_network, target_network, frame_shape, save_path, num_actions, epsilon)
DQNAgent.__init__(self, env, memory, policy_network, target_network, frame_shape, save_path, discount_factor, n_step, double_q, expert_memory, prioritized_replay)
def __init__(self,
env = ProcessedAtariEnv(),
memory = PrioritizedExperienceReplay(),
policy_network = None, #DeepQNetwork(),
target_network = None, #DeepQNetwork(),
frame_shape = (84, 84),
save_path = None,
discount_factor = 0.99,
n_step = None,
double_q = False,
expert_memory = None,
prioritized_replay = False,
num_actions = 4,
eps_schedule = [[1.0, 0.1, 1000000],
[0.1, 0.001, 5000000]]
):
EpsilonAnnealingAgent.__init__(self, env, memory, policy_network, target_network, frame_shape, save_path, num_actions, eps_schedule)
DQNAgent.__init__(self, env, memory, policy_network, target_network, frame_shape, save_path, discount_factor, n_step, double_q, expert_memory, prioritized_replay)
def __init__(self,
env = ProcessedAtariEnv(),
memory = PrioritizedExperienceReplay(),
policy_network = None,#DeepQNetwork(),
target_network = None,#DeepQNetwork(),
frame_shape = (84, 84),
save_path = None,
discount_factor = 0.99,
n_step = None,
double_q = False,
expert_memory = None,
prioritized_replay = False
):
BaseQAgent.__init__(self, env, memory, policy_network, target_network, frame_shape, save_path)
self._idx_range = np.arange(self.memory.batch_size, dtype = np.int32)
self.discount_factor = discount_factor
self.n_step = n_step
self.expert_memory = expert_memory
self.double_q = double_q
self.prioritized_replay = prioritized_replay
import numpy as np
from deep_q_agents import EpsAnnDQNAgent
from deep_q_networks import DeepQNetwork
from experience_replay import PrioritizedExperienceReplay
from atari_preprocessing import atari_enduro_processor, ProcessedAtariEnv
from openai_baseline_wrappers import make_atari, wrap_deepmind
from load_data import LoadAtariHeadData
#create environment
frame_processor = atari_enduro_processor
game_id = 'EnduroNoFrameskip-v4'
env = make_atari(game_id)
env = wrap_deepmind(env)
env = ProcessedAtariEnv(env,
frame_processor,
reward_processor=lambda x: np.sign(x))
# additional env specific parameters
frame_shape = env.reset().shape
frame_skip = 4
num_stacked_frames = 4
num_actions = env.action_space.n
# replay parameters
batch_size = 32
max_frame_num = 2**20
prioritized_replay = True
prio_coeff = 0.6
is_schedule = [0.4, 1.0, 2500000]
replay_epsilon = 0.001
import numpy as np from deep_q_agents import EpsAnnDQNAgent from deep_q_networks import DeepQNetwork from experience_replay import PrioritizedExperienceReplay from atari_preprocessing import atari_pong_processor, ProcessedAtariEnv from openai_baseline_wrappers import make_atari, wrap_deepmind from load_data import LoadAtariHeadData #create environment frame_processor = atari_pong_processor game_id = 'PongNoFrameskip-v4' env = make_atari(game_id) env = wrap_deepmind(env) env = ProcessedAtariEnv(env, frame_processor, reward_processor = lambda x: np.sign(x), neg_reward_terminal = True) # additional env specific parameters frame_shape = env.reset().shape frame_skip = 4 num_stacked_frames = 4 num_actions = env.action_space.n # replay parameters batch_size = 32 max_frame_num = 2**20 prioritized_replay = True prio_coeff = 1.0 is_schedule = [0.0, 0.0, 2500000] replay_epsilon = 0.001 memory_restore_path = None