def env_fn():
#env = gym.make("CartPole-v0")#
env = pistonball_v0.env()
# print(env.action_spaces.values())
# exit(0)
env = supersuit.resize(env, 84,84)
env = supersuit.observation_lambda(env,lambda obs: np.transpose(obs, axes=(2,0,1)))
#env = supersuit.clip_rewards(-1,1)
# env = supersuit.aec_wrappers.pad_observations(env)
# env = supersuit.aec_wrappers.pad_action_space(env)
#env = supersuit.aec_wrappers.continuous_actions(env)
markov_env = aec_to_markov(env)
venv = MarkovVectorEnv(markov_env)
return venv
def env_creator(config):
env = pistonball_v0.env(local_ratio=config.get("local_ratio", 0.2))
env = dtype(env, dtype=float32)
env = color_reduction(env, mode="R")
env = normalize_obs(env)
return env