tf.reset_default_graph()
global_step = tf.Variable(0, name="global_step", trainable=False)
policy_estimator = vpg.PolicyEstimator(env, learning_rate=0.001)
value_estimator = vpg.ValueEstimator(env, learning_rate=0.1)
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
# Note, due to randomness in the policy the number of episodes you need varies
# g = tf.Graph()
# with g.as_default():
# for i in range(10):
policy_estimator = vpg.learn(
env,
policy_estimator,
value_estimator,
max_timesteps=10000,
discount_factor=0.95,
print_freq=1,
outdir="/tmp/experiments/continuous/VPG/other")
# plotting.plot_episode_stats(stats, smoothing_window=10)
# # Try it out
# state = env.reset()
# while 1:
# env.render()
# action = policy_estimator.predict(state)
# next_state, reward, done, _ = env.step(action)
# if done:
# state = env.reset()
# state = next_state
tf.reset_default_graph()
global_step = tf.Variable(0, name="global_step", trainable=False)
policy_estimator = vpg.PolicyEstimator(env, learning_rate=0.001)
value_estimator = vpg.ValueEstimator(env, learning_rate=0.1)
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
# Note, due to randomness in the policy the number of episodes you need varies
# g = tf.Graph()
# with g.as_default():
# for i in range(10):
policy_estimator = vpg.learn(
env,
policy_estimator,
value_estimator,
max_timesteps=100000,
discount_factor=0.95,
print_freq=10,
outdir="/tmp/experiments/VPG/pendulum/100000-2")
# plotting.plot_episode_stats(stats, smoothing_window=10)
# Try it out
state = env.reset()
while 1:
env.render()
action = policy_estimator.predict(state)
next_state, reward, done, _ = env.step(action)
if done:
state = env.reset()
state = next_state
env = gym.envs.make("MountainCarContinuous-v0")
scaler, featurizer = vpg.preprocess(env)
tf.reset_default_graph()
global_step = tf.Variable(0, name="global_step", trainable=False)
policy_estimator = vpg.PolicyEstimator(env, learning_rate=0.001)
value_estimator = vpg.ValueEstimator(env, learning_rate=0.1)
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
# Note, due to randomness in the policy the number of episodes you need varies
# g = tf.Graph()
# with g.as_default():
# for i in range(10):
policy_estimator = vpg.learn(env, policy_estimator, value_estimator, max_timesteps=10000,
discount_factor=0.95,
print_freq=1,
outdir="/tmp/experiments/continuous/VPG/other")
# plotting.plot_episode_stats(stats, smoothing_window=10)
# # Try it out
# state = env.reset()
# while 1:
# env.render()
# action = policy_estimator.predict(state)
# next_state, reward, done, _ = env.step(action)
# if done:
# state = env.reset()
# state = next_state
scaler, featurizer = vpg.preprocess(env)
tf.reset_default_graph()
global_step = tf.Variable(0, name="global_step", trainable=False)
policy_estimator = vpg.PolicyEstimator(env, learning_rate=0.001)
value_estimator = vpg.ValueEstimator(env, learning_rate=0.1)
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
# Note, due to randomness in the policy the number of episodes you need varies
# g = tf.Graph()
# with g.as_default():
# for i in range(10):
policy_estimator = vpg.learn(env, policy_estimator, value_estimator,
max_timesteps=100000,
discount_factor=0.95,
print_freq=10,
outdir="/tmp/experiments/VPG/pendulum/100000-2")
# plotting.plot_episode_stats(stats, smoothing_window=10)
# Try it out
state = env.reset()
while 1:
env.render()
action = policy_estimator.predict(state)
next_state, reward, done, _ = env.step(action)
if done:
state = env.reset()
state = next_state
tf.reset_default_graph()
global_step = tf.Variable(0, name="global_step", trainable=False)
policy_estimator = vpg.PolicyEstimator(env, learning_rate=0.001)
value_estimator = vpg.ValueEstimator(env, learning_rate=0.1)
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
# Note, due to randomness in the policy the number of episodes you need varies
# g = tf.Graph()
# with g.as_default():
# for i in range(10):
policy_estimator = vpg.learn(env,
policy_estimator,
value_estimator,
max_timesteps=args.max_episode_steps,
discount_factor=0.98,
print_freq=10,
outdir="/tmp/experiments/" +
str(args.environment) + "/VPG/")
# plotting.plot_episode_stats(stats, smoothing_window=10)
# # Try it out
# state = env.reset()
# while 1:
# env.render()
# action = policy_estimator.predict(state)
# next_state, reward, done, _ = env.step(action)
# if done:
# state = env.reset()
# state = next_state