# Unable to train on PettingZoo Atari "double\_dunk" environment

**URL:** <https://discuss.ray.io/t/unable-to-train-on-pettingzoo-atari-double-dunk-environment/22438>\
**Category:** RLlib\
**Created:** [May 5, 2025, 9:49pm UTC](https://discuss.ray.io/t/unable-to-train-on-pettingzoo-atari-double-dunk-environment/22438 "2025-05-05T21:49:11Z")\
**Posts on this page:** 1\
**Page:** 1

<div class="post-metadata">

**Author:** ![saasyp](https://avatars.discourse-cdn.com/v4/letter/s/a87d85/32.png) [@saasyp](https://discuss.ray.io/u/saasyp)\
**Post date:** [May 5, 2025, 9:49pm UTC](https://discuss.ray.io/t/unable-to-train-on-pettingzoo-atari-double-dunk-environment/22438/1 "2025-05-05T21:49:11Z")

</div>

**1. Severity of the issue: (select one)**  
 High: Completely blocks me.

**2. Environment:**

- Ray version: 2.45.0
- Python version: 3.9
- OS: MacOS

**3. What happened vs. what you expected:**

- Expected: IAfter` tune.register_env("double_dunk", env_creator)` and calling `config.build_algo()`, RLlib should recognize my custom PettingZoo env and launch training. Overall, I expect a normal PPO training loop on my DoubleDunk-v3 environment without crashes.
- Actual: Immediately after starting, the training crashes returning the following errors:

```auto
EnvError: The env string you provided ('double_dunk_v3') is:
a) Not a supported or -installed environment.
b) Not a tune-registered environment creator.
c) Not a valid env class string.

Try one of the following:
a) For Atari support: `pip install gym[atari] autorom[accept-rom-license]`.
   For PyBullet support: `pip install pybullet`.
b) To register your custom env, do `from ray import tune;
   tune.register_env('[name]', lambda cfg: [return env obj from here using cfg])`.
   Then in your config, do `config.environment(env='[name]').
c) Make sure you provide a fully qualified classpath, e.g.:
   `ray.rllib.examples.envs.classes.repeat_after_me_env.RepeatAfterMeEnv`

```

I have tried all the steps mentioned above, but none of them actually helped me to solve the issue.

This is my code for reproducibility:

```auto
def env_creator(config):

    repeat_p = config.get("repeat_action_probability", 0.25)
    frame_skip = config.get("frame_skip", 4)
    stack = config.get("stack", 4)

    env = double_dunk_v3.parallel_env()
    env = ss.max_observation_v0(env, 2)
    env = ss.sticky_actions_v0(env, repeat_action_probability=repeat_p)
    env = ss.frame_skip_v0(env, frame_skip)
    env = ss.color_reduction_v0(env, mode="full")
    env = ss.resize_v1(env, 84, 84)
    env = ss.frame_stack_v1(env, stack)
    return PettingZooEnv(env)

env_name = "double_dunk_v3"
register_env(env_name, env_creator)

ray.shutdown()
ray.init()

env_temp = env_creator({})
obs_space = env_temp.observation_space
act_space = env_temp.action_space

config = (
    PPOConfig()
    .environment(env_name)
    .framework("torch")
    .env_runners(num_env_runners=0)
    .training(lr=2e-4, train_batch_size_per_learner=2000, num_epochs=10, gamma=0.9)
    .multi_agent(
        policies={"self_play": (None, obs_space, act_space, {})},
        policy_mapping_fn=lambda agent_id, **kw: "self_play"
    )
)

algorithm = config.build_algo()
for i in range(10):
    result = algorithm.train()

```