config.batch_size = 32
config.replay_fn = lambda: ReplayBuffer(
config.eval_env, memory_size=int(1e6), stack=config.history_length)
config.random_action_prob = LinearSchedule(1.0, 0.01, 1e6)
config.state_normalizer = ImageNormalizer()
config.reward_normalizer = SignNormalizer()
config.discount = 0.99
config.target_network_update_freq = 10000
config.exploration_steps = 50000
config.categorical_v_max = 10
config.categorical_v_min = -10
config.categorical_n_atoms = 51
config.rollout_length = 4
config.gradient_clip = 0.5
config.max_steps = 2e7
CategoricalDQNAgent(config).run_steps(
tag=f'{tag}{categorical_dqn_pixel_atari.__name__}-{game}')
if __name__ == '__main__':
random_seed()
select_device(0)
# game = 'MountainCar-v0'
game = 'BreakoutNoFrameskip-v4'
# categorical_dqn_cart_pole()
categorical_dqn_pixel_atari(game, "bench-")
from drl.util.torch_utils import random_seed, select_device
def vpg_cart_pole(game):
config = VPGConfig()
config.num_workers = 5
config.task_fn = lambda: Task(game, num_envs=config.num_workers)
config.eval_env = Task(game)
config.optimizer_fn = lambda params: Adam(params, lr=1e-3)
config.network_fn = lambda: CategoricalActorCriticNet(
config.state_dim, config.action_dim, FCBody(config.state_dim))
config.discount = 0.99
config.use_gae = True
config.gae_tau = 0.97
config.entropy_weight = 0.001
config.rollout_length = 4000
config.gradient_clip = 5
config.logger = get_logger(tag=vpg_cart_pole.__name__)
run_steps(VPGAgent(config))
if __name__ == '__main__':
random_seed(0)
select_device(0)
# game = 'MountainCar-v0'
game = 'CartPole-v0'
# game = 'BreakoutNoFrameskip-v4'
vpg_cart_pole(game)
