0e1f9982cbf60d509e87e324c7076f7b44aeea1d,examples/mujoco_all_sac_real_nvp.py,,run_experiment,#Any#,193

Before Change

def run_experiment(variant):
    if variant["env_name"] == "humanoid-rllab":
        from rllab.envs.mujoco.humanoid_env import HumanoidEnv
        env = normalize(HumanoidEnv())
    elif variant["env_name"] == "swimmer-rllab":
        from rllab.envs.mujoco.swimmer_env import SwimmerEnv
        env = normalize(SwimmerEnv())
    elif variant["env_name"] == "multi-direction-swimmer":
        env = normalize(MultiDirectionSwimmerEnv())
    elif variant["env_name"] == "random-goal-swimmer":
        env = normalize(RandomGoalSwimmerEnv(
            goal_reward_weight=variant["env_goal_reward_weight"]
        ))
    elif variant["env_name"] == "multi-direction-ant":
        env = normalize(MultiDirectionAntEnv())
    elif variant["env_name"] == "multi-direction-humanoid":
        env = normalize(MultiDirectionHumanoidEnv())
    else:
        env = normalize(GymEnv(variant["env_name"]))

    pool = SimpleReplayBuffer(
        env_spec=env.spec,
        max_replay_buffer_size=variant["max_pool_size"],
    )

After Change

def run_experiment(variant):
    env_name = variant["env_name"]
    env_type = env_name.split("-")[-1]

    if "multi-direction" in env_name:
        EnvClass = MULTI_DIRECTION_ENVS[env_type]
        env = normalize(EnvClass())
    elif "random-goal" in env_name:
        EnvClass = RANDOM_GOAL_ENVS[env_type]
        env_args = {
            name.lstrip("env_"): value
            for name, value in variant.items()
            if name.startswith("env_")

Instances