def main():
env = gym.make('BreakoutDeterministic-v4')
frame = env.reset()
env.render()
frames_per_action = 4
num_actions = 4
ATARI_SHAPE_PLUSONE = (105, 80, 5)
num_games = 10
this_states = RingBuffer(5)
this_rewards = RingBuffer(4)
all_prev_states = []
all_next_states = []
all_actions = []
all_rewards = []
all_isterminal = []
# print('a')
prev_frame = preprocess(frame)
for this_game in range(0, num_games):
iter_count = 0
is_done = False
while not is_done:
this_action = env.action_space.sample()
# print('b')
this_action_onehot = action_to_onehot(this_action)
this_states.append(prev_frame)
for action_count in range(0, frames_per_action):
# print('c')
frame, reward, is_done, _ = env.step(this_action)
this_states.append(preprocess(frame))
this_rewards.append(transform_reward(reward))
if not is_done:
env.render()
else:
frame = env.reset()
env.render()
break
prev_frame = frame
if (iter_count > 0):
all_prev_states.append(this_states.clip_from_end(1))
all_next_states.append(this_states.clip_from_start(1))
all_rewards.append(this_rewards)
all_actions.append(this_action)
all_isterminal.append(int(is_done))
# is_done = False
iter_count += 1
# input()
np_prev_states = np.asarray(all_prev_states)
# print('prev states: ',np.shape(np_prev_states))
np_next_states = np.asarray(all_next_states)
# print('next states: ',np.shape(np_next_states))
np_rewards = np.asarray(all_rewards)
# np_rewards = np_rewards[:-1,:]
# print('rewards: ',np.shape(np_rewards))
np_actions = np.asarray(all_actions)
# np_actions = np_actions[:-1]
# print('actions: ',np.shape(np_actions))
np_isterminal = np.asarray(all_isterminal)
# np_isterminal = np_isterminal[:-1]
# print('isterminal: ',np.shape(np_isterminal))
np_num_objects = np.size(np_isterminal)
# print('num_objects:',np_num_objects)
t_model = atari_model(num_actions)
