def main(args):
# environment
env = MuJoCoWrapper(gym.make(args.env), args.reward_scale, args.render)
env.seed(args.seed)
eval_env = MuJoCoWrapper(gym.make(args.env))
eval_env.seed(args.seed)
num_actions = env.action_space.shape[0]
# network parameters
params = TD3NetworkParams(fcs=args.layers,
concat_index=args.concat_index,
state_shape=env.observation_space.shape,
num_actions=num_actions,
gamma=args.gamma,
tau=args.tau,
actor_lr=args.actor_lr,
critic_lr=args.critic_lr,
target_noise_sigma=args.target_noise_sigma,
target_noise_clip=args.target_noise_clip)
# deep neural network
network = TD3Network(params)
# replay buffer
buffer = Buffer(args.buffer_size)
# metrics
saver = tf.train.Saver()
metrics = Metrics(args.name, args.log_adapter, saver)
# exploration noise
noise = NormalActionNoise(np.zeros(num_actions),
np.ones(num_actions) * 0.1)
# controller
controller = TD3Controller(network, buffer, metrics, noise, num_actions,
args.batch_size, args.final_steps,
args.log_interval, args.save_interval,
args.eval_interval)
# view
view = View(controller)
# evaluation
eval_controller = EvalController(network, metrics, args.eval_episode)
eval_view = View(eval_controller)
# save hyperparameters
metrics.log_parameters(vars(args))
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
# save model graph for debugging
metrics.set_model_graph(sess.graph)
if args.load is not None:
saver.restore(sess, args.load)
interact(env, view, eval_env, eval_view)
def main(args):
# environments
env = BatchEnvWrapper(
make_envs(args.env, args.num_envs, args.reward_scale), args.render)
env.seed(args.seed)
eval_env = BatchEnvWrapper(
make_envs(args.env, args.num_envs, args.reward_scale))
eval_env.seed(args.seed)
num_actions = env.action_space.shape[0]
# network parameters
params = PPONetworkParams(fcs=args.layers,
num_actions=num_actions,
state_shape=env.observation_space.shape,
num_envs=args.num_envs,
batch_size=args.batch_size,
epsilon=args.epsilon,
learning_rate=args.lr,
grad_clip=args.grad_clip,
value_factor=args.value_factor,
entropy_factor=args.entropy_factor)
# deep neural network
network = PPONetwork(params)
# rollout buffer
rollout = Rollout()
# metrics
saver = tf.train.Saver()
metrics = Metrics(args.name, args.log_adapter, saver)
# controller
controller = PPOController(network, rollout, metrics, args.num_envs,
args.time_horizon, args.epoch, args.batch_size,
args.gamma, args.lam, args.final_steps,
args.log_interval, args.save_interval,
args.eval_interval)
# view
view = View(controller)
# evaluation
eval_controller = EvalController(network, metrics, args.eval_episodes)
eval_view = View(eval_controller)
# save hyperparameters
metrics.log_parameters(vars(args))
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
# save model graph for debugging
metrics.set_model_graph(sess.graph)
if args.load is not None:
saver.restore(sess, args.load)
interact(env, view, eval_env, eval_view, batch=True)
def main(args):
# environment
env = MuJoCoWrapper(gym.make(args.env), args.reward_scale, args.render)
eval_env = MuJoCoWrapper(gym.make(args.env))
num_actions = env.action_space.shape[0]
# deep neural network
network = SACNetwork(args.layers, args.concat_index,
env.observation_space.shape, num_actions, args.gamma,
args.tau, args.pi_lr, args.q_lr, args.v_lr, args.reg)
# replay buffer
buffer = Buffer(args.buffer_size)
# metrics
saver = tf.train.Saver()
metrics = Metrics(args.name, args.log_adapter, saver)
# exploration noise
noise = EmptyNoise()
# controller
controller = SACController(network, buffer, metrics, noise, num_actions,
args.batch_size, args.final_steps,
args.log_interval, args.save_interval,
args.eval_interval)
# view
view = View(controller)
# evaluation
eval_controller = EvalController(network, metrics, args.eval_episode)
eval_view = View(eval_controller)
# save hyperparameters
metrics.log_parameters(vars(args))
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
# save model graph for debugging
metrics.set_model_graph(sess.graph)
if args.load is not None:
saver.restore(sess, args.load)
interact(env, view, eval_env, eval_view)
def main(args):
env = BatchEnvWrapper(
make_envs(args.env, args.num_envs, args.reward_scale), args.render)
eval_env = BatchEnvWrapper(
make_envs(args.env, args.num_envs, args.reward_scale))
num_actions = env.action_space.shape[0]
network = PPONetwork(args.layers, env.observation_space.shape,
args.num_envs, num_actions, args.batch_size,
args.epsilon, args.lr, args.grad_clip,
args.value_factor, args.entropy_factor)
rollout = Rollout()
saver = tf.train.Saver()
metrics = Metrics(args.name, args.log_adapter, saver)
controller = PPOController(network, rollout, metrics, args.num_envs,
args.time_horizon, args.epoch, args.batch_size,
args.gamma, args.lam, args.final_steps,
args.log_interval, args.save_interval,
args.eval_interval)
view = View(controller)
eval_controller = EvalController(network, metrics, args.eval_episodes)
eval_view = View(eval_controller)
# save hyperparameters
metrics.log_parameters(vars(args))
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
# save model graph for debugging
metrics.set_model_graph(sess.graph)
if args.load is not None:
saver.restore(sess, args.load)
batch_interact(env, view, eval_env, eval_view)