def main(args):
if args.gpu:
ctx = get_extension_context('cudnn', device_id=str(args.device))
nn.set_default_context(ctx)
# atari environment
env = AtariWrapper(gym.make(args.env), args.seed, episodic=True)
eval_env = AtariWrapper(gym.make(args.env), args.seed, episodic=False)
num_actions = env.action_space.n
# action-value function built with neural network
model = NoisyNetDQN(q_function, num_actions, args.batch_size, args.gamma,
args.lr)
if args.load is not None:
nn.load_parameters(args.load)
model.update_target()
buffer = ReplayBuffer(args.buffer_size, args.batch_size)
exploration = ConstantEpsilonGreedy(num_actions, 0.0)
monitor = prepare_monitor(args.logdir)
update_fn = update(model, buffer, args.target_update_interval)
eval_fn = evaluate(eval_env, model, render=args.render)
train(env, model, buffer, exploration, monitor, update_fn, eval_fn,
args.final_step, args.update_start, args.update_interval,
args.save_interval, args.evaluate_interval, ['loss'])
def main(args):
env = gym.make(args.env)
env.seed(args.seed)
eval_env = gym.make(args.env)
eval_env.seed(50)
action_shape = env.action_space.shape
# GPU
if args.gpu:
ctx = get_extension_context('cudnn', device_id=str(args.device))
nn.set_default_context(ctx)
if args.load:
nn.load_parameters(args.load)
model = SAC(env.observation_space.shape, action_shape[0], args.batch_size,
args.critic_lr, args.actor_lr, args.temp_lr, args.tau,
args.gamma)
model.sync_target()
buffer = ReplayBuffer(args.buffer_size, args.batch_size)
monitor = prepare_monitor(args.logdir)
update_fn = update(model, buffer)
eval_fn = evaluate(eval_env, model, render=args.render)
train(env, model, buffer, EmptyNoise(), monitor, update_fn, eval_fn,
args.final_step, args.batch_size, 1, args.save_interval,
args.evaluate_interval, ['critic_loss', 'actor_loss', 'temp_loss'])
def main(args):
env = gym.make(args.env)
env.seed(args.seed)
eval_env = gym.make(args.env)
eval_env.seed(50)
action_shape = env.action_space.shape
# GPU
if args.gpu:
ctx = get_extension_context('cudnn', device_id=str(args.device))
nn.set_default_context(ctx)
if args.load:
nn.load_parameters(args.load)
model = TD3(env.observation_space.shape, action_shape[0], args.batch_size,
args.critic_lr, args.actor_lr, args.tau, args.gamma,
args.target_reg_sigma, args.target_reg_clip)
model.sync_target()
noise = NormalNoise(np.zeros(action_shape),
args.exploration_sigma + np.zeros(action_shape))
buffer = ReplayBuffer(args.buffer_size, args.batch_size)
monitor = prepare_monitor(args.logdir)
update_fn = update(model, buffer, args.update_actor_freq)
eval_fn = evaluate(eval_env, model, render=args.render)
train(env, model, buffer, noise, monitor, update_fn, eval_fn,
args.final_step, args.batch_size, 1, args.save_interval,
args.evaluate_interval, ['critic_loss', 'actor_loss'])
def main(args):
if args.gpu:
ctx = get_extension_context('cudnn', device_id=str(args.device))
nn.set_default_context(ctx)
# atari environment
num_envs = args.num_envs
envs = [gym.make(args.env) for _ in range(num_envs)]
batch_env = BatchEnv([AtariWrapper(env, args.seed) for env in envs])
eval_env = AtariWrapper(gym.make(args.env), 50, episodic=False)
num_actions = envs[0].action_space.n
# action-value function built with neural network
lr_scheduler = learning_rate_scheduler(args.lr, 10**7)
model = A2C(num_actions, num_envs, num_envs * args.time_horizon,
args.v_coeff, args.ent_coeff, lr_scheduler)
if args.load is not None:
nn.load_parameters(args.load)
logdir = prepare_directory(args.logdir)
eval_fn = evaluate(eval_env, model, args.render)
# start training loop
return_fn = compute_returns(args.gamma)
train_loop(batch_env, model, num_actions, return_fn, logdir, eval_fn, args)
