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jblitzar
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github-python

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import os
from logger import Logger
import datetime
import time
def readParser():
parser = argparse.ArgumentParser(description='Diffusion Policy')
parser.add_argument('--env_name', default="Hopper-v3",
help='Mujoco Gym environment (default: Hopper-v3)')
parser.add_argument('--seed', type=int, default=0, metavar='N',
help='random seed (default: 0)')
parser.add_argument('--num_steps', type=int, default=1000000, metavar='N',
help='env timesteps (default: 1000000)')
parser.add_argument('--batch_size', type=int, default=256, metavar='N',
help='batch size (default: 256)')
parser.add_argument('--gamma', type=float, default=0.99, metavar='G',
help='discount factor for reward (default: 0.99)')
parser.add_argument('--tau', type=float, default=0.005, metavar='G',
help='target smoothing coefficient(τ) (default: 0.005)')
parser.add_argument('--update_actor_target_every', type=int, default=1, metavar='N',
help='update actor target per iteration (default: 1)')
parser.add_argument("--policy_type", type=str, default="Diffusion", metavar='S',
help="Diffusion, VAE or MLP")
parser.add_argument("--beta_schedule", type=str, default="cosine", metavar='S',
help="linear, cosine or vp")
parser.add_argument('--n_timesteps', type=int, default=20, metavar='N',
help='diffusion timesteps (default: 20)')
parser.add_argument('--diffusion_lr', type=float, default=0.0001, metavar='G',
help='diffusion learning rate (default: 0.0001)')
parser.add_argument('--critic_lr', type=float, default=0.0003, metavar='G',
help='critic learning rate (default: 0.0003)')
parser.add_argument('--action_lr', type=float, default=0.03, metavar='G',
help='diffusion learning rate (default: 0.03)')
parser.add_argument('--noise_ratio', type=float, default=1.0, metavar='G',
help='noise ratio in sample process (default: 1.0)')
parser.add_argument('--action_gradient_steps', type=int, default=20, metavar='N',
help='action gradient steps (default: 20)')
parser.add_argument('--ratio', type=float, default=0.1, metavar='G',
help='the ratio of action grad norm to action_dim (default: 0.1)')
parser.add_argument('--ac_grad_norm', type=float, default=2.0, metavar='G',
help='actor and critic grad norm (default: 1.0)')
parser.add_argument('--cuda', default='cuda:0',
help='run on CUDA (default: cuda:0)')
parser.add_argument('--alpha_mean', type=float, default=0.001, metavar='G',
help='running mean update weight (default: 0.1)')
parser.add_argument('--alpha_std', type=float, default=0.001, metavar='G',
help='running std update weight (default: 0.001)')
parser.add_argument('--beta', type=float, default=1.0, metavar='G',
help='expQ weight (default: 1.0)')
parser.add_argument('--weighted', action="store_true", help="weighted training")
parser.add_argument('--aug', action="store_true", help="augmentation")
parser.add_argument('--train_sample', type=int, default=64, metavar='N',
help='train_sample (default: 64)')
parser.add_argument('--chosen', type=int, default=1, metavar='N', help="chosen actions (default:1)")
parser.add_argument('--q_neg', type=float, default=0.0, metavar='G', help="q_neg (default: 0.0)")
parser.add_argument('--behavior_sample', type=int, default=4, metavar='N', help="behavior_sample (default: 1)")
parser.add_argument('--target_sample', type=int, default=4, metavar='N',
help="target_sample (default: behavior sample)")
parser.add_argument('--eval_sample', type=int, default=32, metavar='N', help="eval_sample (default: 512)")
parser.add_argument('--deterministic', action="store_true", help="deterministic mode")
parser.add_argument('--q_transform', type=str, default='qadv', metavar='S', help="q_transform (default: qrelu)")
parser.add_argument('--gradient', action="store_true", help="aug gradient")
parser.add_argument('--policy_freq', type=int, default=1, metavar='N', help="policy_freq (default: 1)")
parser.add_argument('--cut', type=float, default=1.0, metavar='G', help="cut (default: 1.0)")
parser.add_argument('--times', type=int, default=1, metavar='N', help="times (default: 1)")
parser.add_argument('--epsilon', type=float, default=0.0, metavar='G', help="eps greedy (default: 0.0)")
parser.add_argument('--entropy_alpha', type=float, default=0.02, metavar='G', help="entropy_alpha (default: 0.02)")
parser.add_argument('--id', type=str, metavar='S', help="id")
parser.add_argument('--render', action="store_true", help="render")
return parser.parse_args()
def evaluate(env, agent, steps, render):
episodes = 10
returns = np.zeros((episodes,), dtype=np.float32)
for i in range(episodes):