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render_mujoco.py
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render_mujoco.py
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"""Visualization of the MuJoCo environments."""
import numpy as np
import random
import torch
import gym
import argparse
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--env", default="HalfCheetah-v2") # OpenAI gym environment name
parser.add_argument("--seed", default=0, type=int) # Sets Gym, PyTorch and Numpy seeds
args = parser.parse_args()
env = gym.make(args.env)
# Set seeds
env.seed(args.seed)
torch.manual_seed(args.seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(args.seed)
np.random.seed(args.seed)
random.seed(args.seed)
state_dim = env.observation_space.shape[0]
action_dim = env.action_space.shape[0]
max_action = float(env.action_space.high[0])
state, done = env.reset(), False
episode_reward = 0
episode_timesteps = 0
episode_num = 0
for episode_num in range(10):
state, done, ep_ret, ep_len = env.reset(), False, 0, 0
while not done:
action = env.action_space.sample()
state, reward, done, _ = env.step(action)
env.render()
ep_ret += reward
ep_len += 1
if done:
episode_num += 1
print(f"Episode Num: {episode_num} Episode T: {ep_len} Reward: {ep_ret:.3f}")