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))