def evaluate():
"""Evaluated current agent, and records a video with it's performance"""
envwrap = GymWrapperFactory.make(FLAGS.env,
actrep=FLAGS.action_repeat,
memlen=FLAGS.memory_len,
w=FLAGS.width,
h=FLAGS.height)
with tf.Session() as sess:
agent = QlearningAgent(session=sess,
action_size=envwrap.action_size,
h=FLAGS.height,
w=FLAGS.width,
channels=FLAGS.memory_len,
opt=tf.train.AdamOptimizer(FLAGS.lr))
sess.run(tf.initialize_all_variables())
if not os.path.exists(FLAGS.logdir):
print('ERROR! No', FLAGS.logdir, 'folder found!')
return
ckpt = tf.train.latest_checkpoint(FLAGS.logdir)
if ckpt is not None:
tf.train.Saver().restore(sess, ckpt)
agent.update_target()
print('Session was restored from %s' % ckpt)
else:
print('ERROR! No checkpoint found at', FLAGS.logdir)
return
#envwrap.env.monitor.start(os.path.join(FLAGS.evaldir, FLAGS.env))
envwrap.env = gym.wrappers.Monitor(
envwrap.env, os.path.join(FLAGS.evaldir, FLAGS.env))
total_reward = 0
for epnum in range(FLAGS.eval_iter):
r_max = []
s = envwrap.reset()
terminal = False
while not terminal:
reward_per_action = agent.predict_rewards(s)
rmax = np.max(reward_per_action)
s, r, terminal, info = envwrap.step(
np.argmax(reward_per_action), test=True)
sreshape = np.transpose(
np.reshape(
s, (FLAGS.width, FLAGS.height, FLAGS.action_repeat)),
[2, 0, 1])
total_reward += r
r_max.append(rmax)
smp.imsave("plots2/" + str(len(r_max)) + "_screen.png",
sreshape[2])
plot2(reward_per_action[:4], len(r_max))
#envwrap.render()
plot(r_max, epnum)
envwrap.env.close()
print('Evaluation finished.')
print('Average reward per episode: %0.4f' %
(total_reward / FLAGS.eval_iter))
def run(worker):
"""Launches worker asynchronously in 'FLAGS.threads' threads
:param worker: worker function"""
print('Starting. Threads:', FLAGS.threads)
processes = []
envs = []
for _ in range(FLAGS.threads):
env = GymWrapperFactory.make(FLAGS.env,
actrep=FLAGS.action_repeat,
memlen=FLAGS.memory_len,
w=FLAGS.width,
h=FLAGS.height)
envs.append(env)
with tf.Session() as sess:
agent = QlearningAgent(session=sess,
action_size=envs[0].action_size,
h=FLAGS.height,
w=FLAGS.width,
channels=FLAGS.memory_len,
opt=tf.train.AdamOptimizer(FLAGS.lr))
saver = tf.train.Saver(tf.global_variables(), max_to_keep=2)
sess.run(tf.global_variables_initializer())
if not os.path.exists(FLAGS.logdir):
os.makedirs(FLAGS.logdir)
ckpt = tf.train.latest_checkpoint(FLAGS.logdir)
if ckpt is not None:
saver.restore(sess, ckpt)
agent.update_target()
print('Restoring session from %s' % ckpt)
summary = AgentSummary(FLAGS.logdir, agent, FLAGS.env)
for i in range(FLAGS.threads):
processes.append(
th.Thread(target=worker,
args=(
agent,
envs[i],
sess,
summary,
saver,
i,
)))
for p in processes:
p.daemon = True
p.start()
while not training_finished:
if FLAGS.render:
for i in range(FLAGS.threads):
envs[i].render()
time.sleep(.01)
for p in processes:
p.join()
def evaluate():
"""Evaluated current agent, and records a video with it's performance"""
envwrap = GymWrapperFactory.make(FLAGS.env,
actrep=FLAGS.action_repeat,
memlen=FLAGS.memory_len,
w=FLAGS.width,
h=FLAGS.height)
with tf.Session() as sess:
agent = QlearningAgent(session=sess,
action_size=envwrap.action_size,
h=FLAGS.height,
w=FLAGS.width,
channels=FLAGS.memory_len,
opt=tf.train.AdamOptimizer(FLAGS.lr))
sess.run(tf.initialize_all_variables())
if not os.path.exists(FLAGS.logdir):
print('ERROR! No', FLAGS.logdir, 'folder found!')
return
ckpt = tf.train.latest_checkpoint(FLAGS.logdir)
if ckpt is not None:
tf.train.Saver().restore(sess, ckpt)
agent.update_target()
print('Session was restored from %s' % ckpt)
else:
print('ERROR! No checkpoint found at', FLAGS.logdir)
return
envwrap.env.monitor.start(os.path.join(FLAGS.evaldir, FLAGS.env))
total_reward = 0
for _ in range(FLAGS.eval_iter):
s = envwrap.reset()
terminal = False
while not terminal:
reward_per_action = agent.predict_rewards(s)
s, r, terminal, info = envwrap.step(
np.argmax(reward_per_action), test=True)
total_reward += r
envwrap.render()
envwrap.env.monitor.close()
print('Evaluation finished.')
print('Average reward per episode: %0.4f' %
(total_reward / FLAGS.eval_iter))