def start(env):
env = gym.make(env)
frames = []
MASTER_NAME = "master-0"
IMAGE_PATH = "images/%s.gif" % env.spec.id
tf.reset_default_graph()
with tf.Session() as session:
with tf.variable_scope(MASTER_NAME) as scope:
env_opts = environments.get_env_options(env, False)
policy = get_policy(env_opts, session)
master_agent = PPOAgent(policy, session, MASTER_NAME, env_opts)
saver = tf.train.Saver(max_to_keep=1)
saver = tf.train.import_meta_graph(
tf.train.latest_checkpoint("models/%s/" % env.spec.id) + ".meta")
saver.restore(session,
tf.train.latest_checkpoint("models/%s/" % env.spec.id))
try:
pass
except:
print("Failed to restore model, starting from scratch")
session.run(tf.global_variables_initializer())
global_step = 0
while global_step < 1000:
terminal = False
s0 = env.reset()
cum_rew = 0
cur_hidden_state = master_agent.get_init_hidden_state()
episode_count = 0
while not terminal:
episode_count += 1
frames.append(env.render(mode='rgb_array'))
action, h_out = master_agent.get_strict_sample(
s0, cur_hidden_state)
cur_hidden_state = h_out
s0, r, terminal, _ = env.step(action)
cum_rew += r
global_step += 1
print(episode_count, cum_rew)
imageio.mimsave(IMAGE_PATH, frames, duration=1.0 / 60.0)
def start(env):
env = gym.make(env)
MASTER_NAME = "master-0"
tf.reset_default_graph()
with tf.Session() as session:
with tf.variable_scope(MASTER_NAME) as scope:
env_opts = environments.get_env_options(env, False)
policy = get_policy(env_opts, session)
master_agent = PPOAgent(policy, session, MASTER_NAME, env_opts)
saver = tf.train.Saver(max_to_keep=1)
saver = tf.train.import_meta_graph(tf.train.latest_checkpoint("models/%s/" % env.spec.id) + ".meta")
saver.restore(session, tf.train.latest_checkpoint("models/%s/" % env.spec.id))
try:
pass
except:
print("Failed to restore model, starting from scratch")
session.run(tf.global_variables_initializer())
while True:
terminal = False
s0 = env.reset()
cum_rew = 0
cur_hidden_state = master_agent.get_init_hidden_state()
episode_count = 0
while not terminal:
episode_count += 1
env.render()
action, h_out = master_agent.get_strict_sample(s0, cur_hidden_state)
cur_hidden_state = h_out
s0, r, terminal, _ = env.step(action)
cum_rew += r
print(episode_count, cum_rew)
