step = 0
while step < arg.game_num:
obs = env.reset()
done = 0
total_reward = 0
step = agent.step_move()
epsilon = max(1 - step * arg.epsilon_decrease, arg.epsilon_min)
while not done:
if np.random.uniform(0, 1) < epsilon:
action = agent.random_action()
else:
action = agent.choose_action(obs)
obs_, reward, done, _ = env.step(
action + 1) # because there is only three action
replay_buffer.store_transition(obs, obs_, action, reward, done)
total_reward += reward
obs = obs_
print('in {}, {}th game: the reward {} '.format(
arg.run_name, step, total_reward))
if step % train_period == 0:
s1, s2, a, r, d = replay_buffer.sample(batch_size=train_batch)
if step % record_period == 0:
loss = agent.train(s1, s2, a, r, d, True)
agent.log_reward(total_reward)
agent.save()
else:
loss = agent.train(s1, s2, a, r, d, False)
print('{}th game: the training loss {}'.format(step, loss))
memory = []
total_reward = 0
obs = env.reset()
frame_processor = FrameProcessor(obs)
obs, reward, done, _ = env.step(agent.random_action() + 1) # do random action at the first frame
total_reward += reward
# play one game
while not done:
input_frame = frame_processor.process(obs)
prob = agent.get_action_prob(input_frame)
action = np.random.choice(3, p=prob)
obs, reward, done, _ = env.step(action + 1)
if reward == 0:
replay_buffer.store_transition(input_frame, action)
else:
total_reward += reward
if reward == 1:
replay_buffer.back_trace_reward(reward, 1)
else:
replay_buffer.back_trace_reward(reward, 0.9)
memory.append(replay_buffer)
replay_buffer = ReplayBuffer(input_shape=[160, 160], start_size=32, max_size=10000000)
# train in the memory
step = agent.step_move()
loss = 0
for j in range(len(memory)):
