Reinforcement Learning, Gaming, Deep Learning.
episode_length=1000): env = gym.make(env_name, batch_size=num_envs, episode_length=episode_length) env = to_torch.JaxToTorchWrapper(env, device=device) env = NormalizeObservation(env) env = NormalizeReward(env) return env env = gym.make('brax-inverted_pendulum-v0', episode_length=1000) env =