def main(args):
# preprocess input state
def preprocess(obser):
'''preprocess 210x160x3 frame into 6400(80x80) flat vector'''
obser = obser[35:195] # 160x160x3
obser = obser[::2, ::2, 0] # downsample (80x80)
obser[obser == 144] = 0
obser[obser == 109] = 0
obser[obser != 0] = 1
return obser.astype(np.float).ravel()
INPUT_DIM = 80 * 80
HIDDEN_UNITS = 200
ACTION_DIM = 6
MAX_EPISODES = 20000
MAX_STEPS = 5000
# load agent
agent = REINFORCE(INPUT_DIM, HIDDEN_UNITS, ACTION_DIM)
agent.construct_model(args.gpu)
# load model or init a new
saver = tf.train.Saver(max_to_keep=1)
if args.model_path is not None:
# reuse saved model
saver.restore(agent.sess, args.model_path)
else:
# build a new model
agent.init_var()
# load env
env = gym.make("Pong-v0")
# evaluation
for ep in xrange(args.ep):
# reset env
total_rewards = 0
state = env.reset()
while True:
env.render()
# preprocess
state = preprocess(state)
# sample actions
action = agent.sample_action(state[np.newaxis, :])
# act!
next_state, reward, done, _ = env.step(action)
total_rewards += reward
# state shift
state = next_state
if done: break
print 'Ep%s Reward: %s ' % (ep + 1, total_rewards)
def main(args):
def preprocess(obs):
obs = obs[35:195]
obs = obs[::2, ::2, 0]
obs[obs == 144] = 0
obs[obs == 109] = 0
obs[obs != 0] = 1
return obs.astype(np.float).ravel()
INPUT_DIM = 80 * 80
HIDDEN_UNITS = 200
ACTION_DIM = 6
# load agent
agent = REINFORCE(INPUT_DIM, HIDDEN_UNITS, ACTION_DIM)
agent.construct_model(args.gpu)
# load model or init a new
saver = tf.train.Saver(max_to_keep=1)
if args.model_path is not None:
# reuse saved model
saver.restore(agent.sess, args.model_path)
else:
# build a new model
agent.init_var()
# load env
env = gym.make('Pong-v0')
# evaluation
for ep in range(args.ep):
# reset env
total_rewards = 0
state = env.reset()
while True:
env.render()
# preprocess
state = preprocess(state)
# sample actions
action = agent.sample_action(state[np.newaxis, :])
# act!
next_state, reward, done, _ = env.step(action)
total_rewards += reward
# state shift
state = next_state
if done:
break
print('Ep%s Reward: %s ' % (ep+1, total_rewards))
def main(args):
def preprocess(obs):
obs = obs[35:195]
obs = obs[::2, ::2, 0]
obs[obs == 144] = 0
obs[obs == 109] = 0
obs[obs != 0] = 1
return obs.astype(np.float).ravel()
INPUT_DIM = 80 * 80
HIDDEN_UNITS = 200
ACTION_DIM = 6
# load agent
agent = REINFORCE(INPUT_DIM, HIDDEN_UNITS, ACTION_DIM)
agent.construct_model(args.gpu)
# load model or init a new
saver = tf.train.Saver(max_to_keep=1)
if args.model_path is not None:
# reuse saved model
saver.restore(agent.sess, args.model_path)
else:
# build a new model
agent.init_var()
# load env
env = gym.make('Pong-v0')
# evaluation
for ep in range(args.ep):
# reset env
total_rewards = 0
state = env.reset()
while True:
env.render()
# preprocess
state = preprocess(state)
# sample actions
action = agent.sample_action(state[np.newaxis, :])
# act!
next_state, reward, done, _ = env.step(action)
total_rewards += reward
# state shift
state = next_state
if done:
break
print('Ep%s Reward: %s ' % (ep + 1, total_rewards))
def main(args):
MODEL_PATH = args.model_path
INPUT_DIM = 80 * 80
HIDDEN_UNITS = 200
ACTION_DIM = 6
MAX_EPISODES = 20000
MAX_STEPS = 5000
# load agent
agent = REINFORCE(INPUT_DIM, HIDDEN_UNITS, ACTION_DIM)
agent.construct_model(args.gpu)
# model saver
saver = tf.train.Saver(max_to_keep=1)
if MODEL_PATH is not None:
saver.restore(agent.sess, args.model_path)
ep_base = int(args.model_path.split('_')[-1])
mean_rewards = float(args.model_path.split('/')[-1].split('_')[0])
else:
agent.sess.run(tf.global_variables_initializer())
ep_base = 0
mean_rewards = None
# load env
env = gym.make('Pong-v0')
# main loop
for ep in range(MAX_EPISODES):
# reset env
total_rewards = 0
state = env.reset()
for step in range(MAX_STEPS):
# preprocess
state = preprocess(state)
# sample actions
action = agent.sample_action(state[np.newaxis, :])
# act!
next_state, reward, done, _ = env.step(action)
total_rewards += reward
agent.store_rollout(state, action, reward)
# state shift
state = next_state
if done:
break
# update model per episode
agent.update_model()
# logging
if mean_rewards is None:
mean_rewards = total_rewards
else:
mean_rewards = 0.99 * mean_rewards + 0.01 * total_rewards
rounds = (21 - np.abs(total_rewards)) + 21
average_steps = (step + 1) / rounds
print('Ep%s: %d rounds \nAvg_steps: %.2f Reward: %s Avg_reward: %.4f' %
(ep + 1, rounds, average_steps, total_rewards, mean_rewards))
if ep % 100 == 0:
if not os.path.isdir(args.save_path):
os.makedirs(args.save_path)
save_name = args.save_path + str(round(mean_rewards, 2)) + '_' \
+ str(ep_base+ep+1)
saver.save(agent.sess, save_name)