def train_double_dqn(env, num_actions):
results_dir = './results/double_dqn/' + game + '_with_bellemare_comments_bonus_1.0'
training_epsilon = 0.1
test_epsilon = 0.05
# frame_history = 1
# enc_func = toy_mr_encoder.encode_toy_mr_state
# # cts_size = (11, 12, 7)
# cts_size = (10, 11, 6)
frame_history = 4
cts_size = (42, 42, 8)
enc_func = atari_encoder.encode_state
dqn = atari_dqn.AtariDQN(frame_history, num_actions, shared_bias=False)
agent = dq_learner_pc.DQLearner(dqn,
num_actions,
target_copy_freq=10000,
frame_history=frame_history,
epsilon_end=training_epsilon,
state_encoder=enc_func,
cts_size=cts_size,
bonus_beta=1.0)
train(agent, env, test_epsilon, results_dir, max_episode_steps=None)
def train_dqn(env, num_actions):
results_dir = './results/dqn/coin_game'
training_epsilon = 0.1
test_epsilon = 0.05
frame_history = 4
dqn = atari_dqn.AtariDQN(frame_history, num_actions, shared_bias=False)
agent = dq_learner.DQLearner(dqn, num_actions, target_copy_freq=10000, epsilon_end=training_epsilon, double=False, frame_history=frame_history)
train(agent, env, test_epsilon, results_dir)
def train_double_dqn(env, num_actions):
results_dir = './results/double_dqn/montezuma_revenge'
training_epsilon = 0.01
test_epsilon = 0.001
frame_history = 4
dqn = atari_dqn.AtariDQN(frame_history, num_actions)
agent = dq_learner.DQLearner(dqn, num_actions, frame_history=frame_history, epsilon_end=training_epsilon)
train(agent, env, test_epsilon, results_dir)
def train_rmax_daqn(env, num_actions):
results_dir = './results/rmax_daqn/%s_fixed_terminal' % game
training_epsilon = 0.01
test_epsilon = 0.001
frame_history = 1
dqn = atari_dqn.AtariDQN(frame_history, num_actions)
agent = rmax_learner.RMaxLearner(env,
env.abstraction,
frame_history=frame_history)
train(agent, env, test_epsilon, results_dir)
def train_double_dqn(env, num_actions, results_dir, game):
training_epsilon = 0.01
test_epsilon = 0.001
frame_history = 4
dqn = atari_dqn.AtariDQN(frame_history, num_actions)
agent = dq_learner.DQLearner(dqn,
num_actions,
frame_history=frame_history,
epsilon_end=training_epsilon)
train(agent, env, test_epsilon, results_dir, game)
def train_double_dqn(env, num_actions):
results_dir = './results/dqn/%s_single_life_part_2' % game
training_epsilon = 0.01
test_epsilon = 0.001
# frame_history = 1
frame_history = 4
dqn = atari_dqn.AtariDQN(frame_history, num_actions)
with tf.device('/gpu:0'):
agent = dq_learner.DQLearner(dqn, num_actions, beta=1.0, frame_history=frame_history, epsilon_end=training_epsilon,
restore_network_file='results/dqn/mr_single_life/mr_best_net')
train(agent, env, test_epsilon, results_dir)
def train_dqn(env, num_actions):
results_dir = './results/dqn/' + game + '_vanilla'
training_epsilon = 0.1
test_epsilon = 0.05
# frame_history = 1
# enc_func = toy_mr_encoder.encode_toy_mr_state
# cts_size = (11, 12, 7)
frame_history = 4
cts_size = (42, 42, 8)
enc_func = atari_encoder.encode_state
dqn = atari_dqn.AtariDQN(frame_history, num_actions, shared_bias=False)
agent = dq_learner_pc.DQLearner(dqn, num_actions, target_copy_freq=10000, epsilon_end=training_epsilon, double=False, frame_history=frame_history,
state_encoder=enc_func, cts_size=cts_size)
train(agent, env, test_epsilon, results_dir)
