Gym for multi-agent reinforcement learning

Farama Foundation

Last update: Jan 9, 2023

Related tags

Deep Learning PettingZoo

Overview

PettingZoo is a Python library for conducting research in multi-agent reinforcement learning, akin to a multi-agent version of Gym.

Our website, with comprehensive documentation, is pettingzoo.ml

Environments and Installation

PettingZoo includes the following families of environments:

Atari: Multi-player Atari 2600 games (cooperative, competitive and mixed sum)
Butterfly: Cooperative graphical games developed by us, requiring a high degree of coordination
Classic: Classical games including card games, board games, etc.
MAgent: Configurable environments with massive numbers of particle agents, originally from https://github.com/geek-ai/MAgent
MPE: A set of simple nongraphical communication tasks, originally from https://github.com/openai/multiagent-particle-envs
SISL: 3 cooperative environments, originally from https://github.com/sisl/MADRL

To install the pettingzoo base library, use pip install pettingzoo

This does not include dependencies for all families of environments (there's a massive number, and some can be problematic to install on certain systems). You can install these dependencies for one family like pip install pettingzoo[atari] or use pip install pettingzoo[all] to install all dependencies.

We support Python 3.7, 3.8 and 3.9 on Linux and macOS. We will accept PRs related to Windows, but do not officially support it.

API

PettingZoo model environments as Agent Environment Cycle (AEC) games, in order to be able to cleanly support all types of multi-agent RL environments under one API and to minimize the potential for certain classes of common bugs.

Using environments in PettingZoo is very similar to Gym, i.e. you initialize an environment via:

from pettingzoo.butterfly import pistonball_v5
env = pistonball_v5.env()

Environments can be interacted with in a manner very similar to Gym:

env.reset()
for agent in env.agent_iter():
    observation, reward, done, info = env.last()
    action = policy(observation)
    env.step(action)

For the complete API documentation, please see https://www.pettingzoo.ml/api

Parallel API

In certain environments, it's a valid to assume that agents take their actions at the same time. For these games, we offer a secondary API to allow for parallel actions, documented at https://www.pettingzoo.ml/api#parallel-api

SuperSuit

SuperSuit is a library that includes all commonly used wrappers in RL (frame stacking, observation, normalization, etc.) for PettingZoo and Gym environments with a nice API. We developed it in lieu of wrappers built into PettingZoo. https://github.com/Farama-Foundation/SuperSuit

Environment Versioning

PettingZoo keeps strict versioning for reproducibility reasons. All environments end in a suffix like "_v0". When changes are made to environments that might impact learning results, the number is increased by one to prevent potential confusion.

Citation

To cite this project in publication, please use

@article{terry2020pettingzoo,
  Title = {PettingZoo: Gym for Multi-Agent Reinforcement Learning},
  Author = {Terry, J. K and Black, Benjamin and Grammel, Nathaniel and Jayakumar, Mario and Hari, Ananth and Sulivan, Ryan and Santos, Luis and Perez, Rodrigo and Horsch, Caroline and Dieffendahl, Clemens and Williams, Niall L and Lokesh, Yashas and Sullivan, Ryan and Ravi, Praveen},
  journal={arXiv preprint arXiv:2009.14471},
  year={2020}
}

Comments

Error with Tiger-Deer Env: Selecting Invalid Agent.

Hi,

I've been running the MAgent Tiger-Deer environment with 2 different algorithms: a RandomLearner and rllib's PPO. I'm also currently using rllib's PettingZooEnv. It seems both of the algorithms work for some number of iterations, but then error-out in this line https://github.com/ray-project/ray/blob/master/rllib/env/pettingzoo_env.py#L161.

The issue is that the agent being selected, deer_92, is not in the action_dict. I checked the self.aec_env.dones dict, however, and the agent is there. I added a snippet of the output, below. I printed out relevant info (shown after each == start step ==) when entering the step() function. Furthermore, it also appears all steps prior to this error only select deer_0 as the agent. I've re-ran the experiment several times and it always has the same result (e.g., deer_0 is always chosen and then it errors-out once any other agent is chosen).

I'm not sure if this is an issue with rllib, the Tiger-Deer env, or my own config.

[2m[36m(pid=34152)[0m =============== start step =====================
[2m[36m(pid=34152)[0m self.aec_env.agent_selection --> deer_0
[2m[36m(pid=34152)[0m stepped_agents --> set()
[2m[36m(pid=34152)[0m list(action_dict) --> ['deer_0', 'deer_1', 'deer_2', 'deer_3', 'deer_4', 'deer_5', 'deer_6', 'deer_7', 'deer_8', 'deer_9', 'deer_10', 'deer_11', 'deer_12', 'deer_13', 'deer_14', 'deer_15', 'deer_16', 'deer_17', 'deer_18', 'deer_19', 'deer_20', 'deer_21', 'deer_22', 'deer_23', 'deer_24', 'deer_25', 'deer_26', 'deer_27', 'deer_28', 'deer_29', 'deer_30', 'deer_31', 'deer_32', 'deer_33', 'deer_34', 'deer_35', 'deer_36', 'deer_37', 'deer_38', 'deer_39', 'deer_40', 'deer_41', 'deer_42', 'deer_43', 'deer_44', 'deer_45', 'deer_46', 'deer_47', 'deer_48', 'deer_49', 'deer_50', 'deer_51', 'deer_52', 'deer_53', 'deer_54', 'deer_55', 'deer_56', 'deer_57', 'deer_58', 'deer_59', 'deer_60', 'deer_61', 'deer_62', 'deer_63', 'deer_64', 'deer_65', 'deer_66', 'deer_67', 'deer_68', 'deer_69', 'deer_70', 'deer_71', 'deer_72', 'deer_73', 'deer_74', 'deer_75', 'deer_76', 'deer_77', 'deer_78', 'deer_79', 'deer_80', 'deer_81', 'deer_82', 'deer_83', 'deer_84', 'deer_85', 'deer_86', 'deer_87', 'deer_88', 'deer_89', 'deer_90', 'deer_91', 'deer_92', 'deer_93', 'deer_94', 'deer_95', 'deer_96', 'deer_97', 'deer_98', 'deer_99', 'deer_100', 'tiger_0', 'tiger_1', 'tiger_2', 'tiger_3', 'tiger_4', 'tiger_5', 'tiger_6', 'tiger_7', 'tiger_8', 'tiger_9', 'tiger_10', 'tiger_11', 'tiger_12', 'tiger_13', 'tiger_14', 'tiger_15', 'tiger_16', 'tiger_17', 'tiger_18', 'tiger_19']
[2m[36m(pid=34152)[0m agent in action_dict -->  True
[2m[36m(pid=34152)[0m agent in self.aec_env.dones --> False
[2m[36m(pid=34152)[0m =============== start step =====================
[2m[36m(pid=34152)[0m self.aec_env.agent_selection --> deer_0
[2m[36m(pid=34152)[0m stepped_agents --> set()
[2m[36m(pid=34152)[0m list(action_dict) --> ['deer_0', 'deer_1', 'deer_2', 'deer_3', 'deer_4', 'deer_5', 'deer_6', 'deer_7', 'deer_8', 'deer_9', 'deer_10', 'deer_11', 'deer_12', 'deer_13', 'deer_14', 'deer_15', 'deer_16', 'deer_17', 'deer_18', 'deer_19', 'deer_20', 'deer_21', 'deer_22', 'deer_23', 'deer_24', 'deer_25', 'deer_26', 'deer_27', 'deer_28', 'deer_29', 'deer_30', 'deer_31', 'deer_32', 'deer_33', 'deer_34', 'deer_35', 'deer_36', 'deer_37', 'deer_38', 'deer_39', 'deer_40', 'deer_41', 'deer_42', 'deer_43', 'deer_44', 'deer_45', 'deer_46', 'deer_47', 'deer_48', 'deer_49', 'deer_50', 'deer_51', 'deer_52', 'deer_53', 'deer_54', 'deer_55', 'deer_56', 'deer_57', 'deer_58', 'deer_59', 'deer_60', 'deer_61', 'deer_62', 'deer_63', 'deer_64', 'deer_65', 'deer_66', 'deer_67', 'deer_68', 'deer_69', 'deer_70', 'deer_71', 'deer_72', 'deer_73', 'deer_74', 'deer_75', 'deer_76', 'deer_77', 'deer_78', 'deer_79', 'deer_80', 'deer_81', 'deer_82', 'deer_83', 'deer_84', 'deer_85', 'deer_86', 'deer_87', 'deer_88', 'deer_89', 'deer_90', 'deer_91', 'deer_92', 'deer_93', 'deer_94', 'deer_95', 'deer_96', 'deer_97', 'deer_98', 'deer_99', 'deer_100', 'tiger_0', 'tiger_1', 'tiger_2', 'tiger_3', 'tiger_4', 'tiger_5', 'tiger_6', 'tiger_7', 'tiger_8', 'tiger_9', 'tiger_10', 'tiger_11', 'tiger_12', 'tiger_13', 'tiger_14', 'tiger_15', 'tiger_16', 'tiger_17', 'tiger_18', 'tiger_19']
[2m[36m(pid=34152)[0m agent in action_dict -->  True
[2m[36m(pid=34152)[0m agent in self.aec_env.dones -->  False
[2m[36m(pid=34152)[0m =============== start step =====================
[2m[36m(pid=34152)[0m self.aec_env.agent_selection --> deer_92
[2m[36m(pid=34152)[0m stepped_agents --> set()
[2m[36m(pid=34152)[0m list(action_dict) --> ['deer_0', 'deer_1', 'deer_2', 'deer_3', 'deer_4', 'deer_5', 'deer_6', 'deer_7', 'deer_8', 'deer_9', 'deer_10', 'deer_11', 'deer_12', 'deer_13', 'deer_14', 'deer_15', 'deer_16', 'deer_17', 'deer_18', 'deer_19', 'deer_20', 'deer_21', 'deer_22', 'deer_23', 'deer_24', 'deer_25', 'deer_26', 'deer_27', 'deer_28', 'deer_29', 'deer_30', 'deer_31', 'deer_32', 'deer_33', 'deer_34', 'deer_35', 'deer_36', 'deer_37', 'deer_38', 'deer_39', 'deer_40', 'deer_41', 'deer_42', 'deer_43', 'deer_44', 'deer_45', 'deer_46', 'deer_47', 'deer_48', 'deer_49', 'deer_50', 'deer_51', 'deer_52', 'deer_53', 'deer_54', 'deer_55', 'deer_56', 'deer_57', 'deer_58', 'deer_59', 'deer_60', 'deer_61', 'deer_62', 'deer_63', 'deer_64', 'deer_65', 'deer_66', 'deer_67', 'deer_68', 'deer_69', 'deer_70', 'deer_71', 'deer_72', 'deer_73', 'deer_74', 'deer_75', 'deer_76', 'deer_77', 'deer_78', 'deer_79', 'deer_80', 'deer_81', 'deer_82', 'deer_83', 'deer_84', 'deer_85', 'deer_86', 'deer_87', 'deer_88', 'deer_89', 'deer_90', 'deer_91', 'deer_93', 'deer_94', 'deer_95', 'deer_96', 'deer_97', 'deer_98', 'deer_99', 'deer_100', 'tiger_0', 'tiger_1', 'tiger_2', 'tiger_3', 'tiger_4', 'tiger_5', 'tiger_6', 'tiger_7', 'tiger_8', 'tiger_9', 'tiger_10', 'tiger_11', 'tiger_12', 'tiger_13', 'tiger_14', 'tiger_15', 'tiger_16', 'tiger_17', 'tiger_18', 'tiger_19']
[2m[36m(pid=34152)[0m agent in action_dict -->  False
[2m[36m(pid=34152)[0m agent in self.aec_env.dones -->  True
== Status ==
Memory usage on this node: 24.8/377.6 GiB
Using FIFO scheduling algorithm.
Resources requested: 0/80 CPUs, 0/2 GPUs, 0.0/252.88 GiB heap, 0.0/77.54 GiB objects (0/1.0 GPUType:V100)
Result logdir: /home/ray_results/Campaign_Tiger-Deer-v1
Number of trials: 1 (1 ERROR)
+------------------------------------------+----------+-------+--------+------------------+------+----------+
| Trial name                               | status   | loc   |   iter |   total time (s) |   ts |   reward |
|------------------------------------------+----------+-------+--------+------------------+------+----------|
| PS_PPO_Trainer_Tiger-Deer-v1_41b65_00000 | ERROR    |       |      3 |          3576.35 | 3672 | 0.166667 |
+------------------------------------------+----------+-------+--------+------------------+------+----------+
Number of errored trials: 1

If I use the PettingZooEnv in version in ray==0.87, the error is https://github.com/ray-project/ray/blob/releases/0.8.7/rllib/env/pettingzoo_env.py#L165.

Lastly, I also applied the following SuperSuit wrappers: pad_observations_v0, pad_action_space_v0, agent_indicator_v0, and flatten_v0, and I'm running PettingZoo==1.3.3 and SuperSuit==2.1.0.

Thanks.

opened by jdpena 20

MPE Continuous Action support

I've added support for continuous actions in MPE as discussed in #249 through the continuous_actions=False argument in the environment config.

I've tested my changes on all environments using RLLib MADDPG, and run ./release_test.sh. The latter needed some updates as well.

All environments are working except simple_world_comm_v2, which seems to have a supersuit-related bug.

opened by Rohan138 19
Rps merge
Made rps sequential with argument max_cycles.

Merged rpsls in rps. Choose between both env with argument lizard_spock

I am not sure if the rps version needs to be bumped or not. Also, should the name of the env still be called rps despite having merged rpsls in the same env?
opened by rodrigodelazcano 17
Move Pyright to `pre-commit` + add `pydocstyle`
Description

This PR moves the Pyright checks from CI only to pre-commit (both local + CI), fixes some typing issues, and add also pydocstyle to pre-commit.

Type of change

Refactoring/maintenance

Checklist:

[x] I have run the pre-commit checks with pre-commit run --all-files (see CONTRIBUTING.md instructions to set it up)

[x] I have run pytest -v and no errors are present.

[x] I have commented my code, particularly in hard-to-understand areas

[x] I have made corresponding changes to the documentation

[x] I solved any possible warnings that pytest -v has generated that are related to my code to the best of my knowledge.

[x] New and existing unit tests pass locally with my changes
opened by kir0ul 16

Error running tutorial: 'ProcConcatVec' object has no attribute 'pipes'

I'm running into an error with this long stack trace when I try to run the 13 line tutorial:

/Users/erick/.local/share/virtualenvs/rl-0i49mzF7/lib/python3.9/site-packages/torch/utils/tensorboard/__init__.py:4: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
  if not hasattr(tensorboard, '__version__') or LooseVersion(tensorboard.__version__) < LooseVersion('1.15'):
/Users/erick/.local/share/virtualenvs/rl-0i49mzF7/lib/python3.9/site-packages/torch/utils/tensorboard/__init__.py:4: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
  if not hasattr(tensorboard, '__version__') or LooseVersion(tensorboard.__version__) < LooseVersion('1.15'):
Traceback (most recent call last):
  File "<string>", line 1, in <module>
  File "/usr/local/Cellar/[email protected]/3.9.12/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/spawn.py", line 116, in spawn_main
    exitcode = _main(fd, parent_sentinel)
  File "/usr/local/Cellar/[email protected]/3.9.12/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/spawn.py", line 125, in _main
    prepare(preparation_data)
  File "/usr/local/Cellar/[email protected]/3.9.12/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/spawn.py", line 236, in prepare
    _fixup_main_from_path(data['init_main_from_path'])
  File "/usr/local/Cellar/[email protected]/3.9.12/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/spawn.py", line 287, in _fixup_main_from_path
    main_content = runpy.run_path(main_path,
  File "/usr/local/Cellar/[email protected]/3.9.12/Frameworks/Python.framework/Versions/3.9/lib/python3.9/runpy.py", line 268, in run_path
    return _run_module_code(code, init_globals, run_name,
  File "/usr/local/Cellar/[email protected]/3.9.12/Frameworks/Python.framework/Versions/3.9/lib/python3.9/runpy.py", line 97, in _run_module_code
    _run_code(code, mod_globals, init_globals,
  File "/usr/local/Cellar/[email protected]/3.9.12/Frameworks/Python.framework/Versions/3.9/lib/python3.9/runpy.py", line 87, in _run_code
    exec(code, run_globals)
  File "/Users/erick/dev/rl/main_pettingzoo.py", line 22, in <module>
    env = ss.concat_vec_envs_v1(env, 8, num_cpus=4, base_class="stable_baselines3")
  File "/Users/erick/.local/share/virtualenvs/rl-0i49mzF7/lib/python3.9/site-packages/supersuit/vector/vector_constructors.py", line 60, in concat_vec_envs_v1
    vec_env = MakeCPUAsyncConstructor(num_cpus)(*vec_env_args(vec_env, num_vec_envs))
  File "/Users/erick/.local/share/virtualenvs/rl-0i49mzF7/lib/python3.9/site-packages/supersuit/vector/constructors.py", line 38, in constructor
    return ProcConcatVec(
  File "/Users/erick/.local/share/virtualenvs/rl-0i49mzF7/lib/python3.9/site-packages/supersuit/vector/multiproc_vec.py", line 144, in __init__
    proc.start()
  File "/usr/local/Cellar/[email protected]/3.9.12/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/process.py", line 121, in start
    self._popen = self._Popen(self)
  File "/usr/local/Cellar/[email protected]/3.9.12/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/context.py", line 224, in _Popen
    return _default_context.get_context().Process._Popen(process_obj)
  File "/usr/local/Cellar/[email protected]/3.9.12/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/context.py", line 284, in _Popen
    return Popen(process_obj)
  File "/usr/local/Cellar/[email protected]/3.9.12/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/popen_spawn_posix.py", line 32, in __init__
    super().__init__(process_obj)
  File "/usr/local/Cellar/[email protected]/3.9.12/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/popen_fork.py", line 19, in __init__
    self._launch(process_obj)
  File "/usr/local/Cellar/[email protected]/3.9.12/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/popen_spawn_posix.py", line 42, in _launch
    prep_data = spawn.get_preparation_data(process_obj._name)
  File "/usr/local/Cellar/[email protected]/3.9.12/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/spawn.py", line 154, in get_preparation_data
    _check_not_importing_main()
  File "/usr/local/Cellar/[email protected]/3.9.12/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/spawn.py", line 134, in _check_not_importing_main
    raise RuntimeError('''
RuntimeError: 
        An attempt has been made to start a new process before the
        current process has finished its bootstrapping phase.

        This probably means that you are not using fork to start your
        child processes and you have forgotten to use the proper idiom
        in the main module:

            if __name__ == '__main__':
                freeze_support()
                ...

        The "freeze_support()" line can be omitted if the program
        is not going to be frozen to produce an executable.
Exception ignored in: <function ProcConcatVec.__del__ at 0x112cf1310>
Traceback (most recent call last):
  File "/Users/erick/.local/share/virtualenvs/rl-0i49mzF7/lib/python3.9/site-packages/supersuit/vector/multiproc_vec.py", line 210, in __del__
    for pipe in self.pipes:
AttributeError: 'ProcConcatVec' object has no attribute 'pipes'

Maybe the issue here is some mismatch in library versioning, but I found no reference to which supersuit version is supposed to run with the tutorial (or with the rest of the code).

I am running python 3.9 with supersuit 3.4 and pettingzoo 1.18.1

opened by erickrf 16

Import env errors

Hello,

I am new to MARL and want to create custom parallel env in PettingZoo and wanted to play with it. I am getting the following import error when attempting to get the environments.

from pettingzoo.butterfly import pistonball_v4 env = pistonball_v4.env()

` ImportError: cannot import name 'pistonball_v4' from 'pettingzoo.butterfly' PS C:\Users\mvadr\Desktop\gym>

`
bug

opened by mvadrev 13
Hanabi Integration
Hanabi, integrated from official repo as git submodule.

See hanabi/README.md for how to pull and setup git submodule.

See documentation within class for full information.

Debugged and E2E tested with unittest, including api test as part of the unit tests.

CHANGE REQUEST: Add typing annotation in utils/env.py, see last commit.

enhancement
opened by dissendahl 13

[Bug Report] cannot import name 'Renderer' from 'magent'

If you are submitting a bug report, please fill in the following details and use the tag [bug].

Describe the bug I try to change the environment based on this script. The origin script used waterworld which has bug so I change it to magent.battle. The output show below:


Traceback (most recent call last):
  File "rlpe.py", line 4, in <module>
    from pettingzoo.magent import battle_v4
  File "/anaconda3/envs/PettingZoo/lib/python3.7/site-packages/pettingzoo/magent/__init__.py", line 5, in __getattr__
    return deprecated_handler(env_name, __path__, __name__)
  File "/anaconda3/envs/PettingZoo/lib/python3.7/site-packages/pettingzoo/utils/deprecated_module.py", line 52, in deprecated_handler
    spec.loader.exec_module(module)
  File "/anaconda3/envs/PettingZoo/lib/python3.7/site-packages/pettingzoo/magent/battle_v4.py", line 1, in <module>
    from .battle.battle import env, parallel_env, raw_env  # noqa: F401
  File "/anaconda3/envs/PettingZoo/lib/python3.7/site-packages/pettingzoo/magent/battle/battle.py", line 7, in <module>
    from pettingzoo.magent.magent_env import magent_parallel_env, make_env
  File "/anaconda3/envs/PettingZoo/lib/python3.7/site-packages/pettingzoo/magent/magent_env.py", line 4, in <module>
    from magent import Renderer
ImportError: cannot import name 'Renderer' from 'magent' (/anaconda3/envs/PettingZoo/lib/python3.7/site-packages/magent/__init__.py)

Code example

from ray import tune
from ray.tune.registry import register_env
from ray.rllib.env.wrappers.pettingzoo_env import PettingZooEnv
from pettingzoo.magent import battle_v4

# Based on code from github.com/parametersharingmadrl/parametersharingmadrl

if __name__ == "__main__":
    # RDQN - Rainbow DQN
    # ADQN - Apex DQN
    def env_creator(args):
        return PettingZooEnv(battle_v4.env(map_size=45, minimap_mode=False, step_reward=-0.005,
dead_penalty=-0.1, attack_penalty=-0.1, attack_opponent_reward=0.2,
max_cycles=1000, extra_features=False))

    env = env_creator({})
    register_env("battle", env_creator)

    tune.run(
        "APEX_DDPG",
        stop={"episodes_total": 60000},
        checkpoint_freq=10,
        config={
            # Enviroment specific
            "env": "battle",
            # General
            "num_gpus": 1,
            "num_workers": 2,
            # Method specific
            "multiagent": {
                "policies": set(env.agents),
                "policy_mapping_fn": (lambda agent_id, episode, **kwargs: agent_id),
            },
        },
    )

System Info Describe the characteristic of your environment: Linux with conda and pip Python 3.7.9 PettingZoo 1.20.1 supersuit 3.5.0 gym 0.22.0 Additional context Add any other context about the problem here.

Checklist

[x] I have checked that there is no similar issue in the repo (required)

help wanted

opened by hellofinch 12

Refactor Pytest multiple calls to a single call
Description

This PR fixes issue https://github.com/Farama-Foundation/PettingZoo/issues/719. The main changes are the following:

Removed release_test.sh

Made according changes to the docs

Bumped GH actions versions

Updated the tests section in setup.py

Type of change

Refactoring

This change requires a documentation update

Checklist:

[x] I have run the pre-commit checks with pre-commit run --all-files (see CONTRIBUTING.md instructions to set it up)

[x] I have run ./release_test.sh and no errors are present.

[x] I have commented my code, particularly in hard-to-understand areas

[x] I have made corresponding changes to the documentation

[x] I solved any possible warnings that ./release_test.sh has generated that are related to my code to the best of my knowledge.
opened by kir0ul 12
How to set black_death to true

Hi, I have a custom pettingzoo Parallel environment and want to train a shared policy on multiple agents. I want to be able to have a few of my agents inactive during a training-episode under certain conditions. I also want to bring back agents during an episode. I call the petting_zoo_env_to_vec_env_v1 and concat_vec_envs_v1 on my environment to allow for shared policy and training on copies of the environment.

I also use the wrappers black_death_v3, pad_observations_v0 and pad_action_space_v0 to allow for agent death but I still get an error message "environment has agent death. Not allowed for pettingzoo_env_to_vec_env_v1 unless black_death is true". How do I set this? So in principle is it possible to have self.possible_agents as a sort of agent pool to take agents from and then have self.agents as the active agents during an episode?

best regards

Axel
question

opened by Axelskool 12
Chess Environment Trailing History
I was trying to read through and understand the chess environment before attempting to create my own environment for MARL problem. I was reading through the step function and think that the environment is archiving a trailing history.

def step(self, action): if self.dones[self.agent_selection]: return self._was_done_step(action) current_agent = self.agent_selection current_index = self.agents.index(current_agent) next_board = chess_utils.get_observation(self.board, current_agent) self.board_history = np.dstack((next_board[:, :, 7:], self.board_history[:, :, :-13])) ....

When the step function is first called by the white pieces, the starting board is added to the board_history as it is run prior to any move being made. When the observe function is then run by the black pieces, the observation would fail to include the first move made by white pieces. Is there anything I am missing?
bug
opened by 01jongmin 12

[Bug Report] AttributeError: accessing private attribute '_cumulative_rewards' is prohibited

Describe the bug

When creating a wrapper around an environment and overriding only the seed method, when I try to call the .last() method of the wrapped environment I get: AttributeError: accessing private attribute '_cumulative_rewards' is prohibited. It seems the error only happens if the environment has an observation space with spaces.Sequence in it, as this does not happen on other pettinzoo environments and spaces that I tested.

I managed to re-create the issue on this colab notebook, I have also attached the same code below.

Code example

from typing import Optional, Any, Dict, List

from gymnasium import Space, spaces
from pettingzoo import AECEnv
from pettingzoo.utils import BaseWrapper


class TestEnv(AECEnv):
    metadata = {'render.modes': ['ansi'], 'name': 'test_env'}

    def __init__(self, seed: Optional[int] = None):
        super().__init__()
        self.n_cards = 52
        self.cards = []

        self.possible_agents: List[str] = ["player_0"]
        self.agents = self.possible_agents.copy()

        self.action_spaces = {agent: spaces.Discrete(self.n_cards) for agent in self.agents}
        # The bug seems to be when using spaces.Sequence
        self.observation_spaces = {agent: spaces.Sequence(spaces.Discrete(self.n_cards)) for agent in self.agents}
        self.infos = {i: {} for i in self.agents}

        self.reset(seed)

    def reset(self, seed: Optional[int] = None, return_info: bool = False, options: Optional[Dict] = None) -> None:
        self.cards = []

        self.agents = self.possible_agents.copy()
        self.rewards = {agent: 0 for agent in self.agents}
        self._cumulative_rewards = {agent: 0 for agent in self.agents}
        self.truncations = {i: False for i in self.agents}

    def seed(self, seed: Optional[int] = None) -> None:
        pass

    def observation_space(self, agent: str) -> Space:
        return self.observation_spaces[agent]

    def action_space(self, agent: str) -> Space:
        return self.action_spaces[agent]

    @property
    def agent_selection(self) -> str:
        return self.agents[0]

    @property
    def terminations(self) -> Dict[str, bool]:
        return dict([(agent, False) for agent in self.agents])

    def observe(self, agent: str) -> List[Any]:
        return self.cards

    def step(self, action: int) -> None:
        assert action in self.action_spaces[self.agent_selection]
        self.cards.append(action)

    def render(self) -> str:
        return self.cards.__repr__()

    def state(self):
        pass

    def close(self):
        super().close()


class TestEnvWrapper(BaseWrapper):
    def seed(self, seed: Optional[int] = None) -> None:
        pass



# The env works
print(TestEnv().last())

# The wrapper has an error
TestEnvWrapper(TestEnv()).last()

System info

Pettingzoo version: 1.22.3

Additional context

No response

Checklist

[X] I have checked that there is no similar issue in the repo

bug

opened by LetteraUnica 0

Fix typo: `BaseParallelWraper` renamed to `BaseParallelWrapper`
Description

As title, fix typo of BaseParallelWrapper (previously BaseParallelWraper). Linked to its SuperSuit counterpart https://github.com/Farama-Foundation/SuperSuit/pull/204.

Type of change

Bug fix (non-breaking change which fixes an issue)

Checklist:

[x] I have run the pre-commit checks with pre-commit run --all-files (see CONTRIBUTING.md instructions to set it up)

[x] I have run pytest -v and no errors are present.

[x] I have commented my code, particularly in hard-to-understand areas

[ ] I have made corresponding changes to the documentation

[ ] I solved any possible warnings that pytest -v has generated that are related to my code to the best of my knowledge.

[ ] I have added tests that prove my fix is effective or that my feature works

[ ] New and existing unit tests pass locally with my changes
opened by mikcnt 4
Fixed: black screen rendering for MPE env in rgb_array mode
Description

Fixes #864 by drawing on pygame screen before extracting the image for rgb_array mode. Only calls pygame.surfarray.pixels3d when needed.

Not sure if a new unit test is needed, can add if necessary !

Type of change

Bug fix (non-breaking change which fixes an issue)

Screenshots

Please attach before and after screenshots of the change if applicable.

Before:

After:

Checklist:

[x] I have run the pre-commit checks with pre-commit run --all-files (see CONTRIBUTING.md instructions to set it up)

[x] I have run pytest -v and no errors are present.

[ ] I have commented my code, particularly in hard-to-understand areas

[ ] I have made corresponding changes to the documentation

[ ] I solved any possible warnings that pytest -v has generated that are related to my code to the best of my knowledge.

[ ] I have added tests that prove my fix is effective or that my feature works

[ ] New and existing unit tests pass locally with my changes
opened by cibeah 0
[Proposal] Support `python3.11`
Proposal

support python3.11

Motivation

3.11 is the latest stable python release,

Gymnasium already supports it

we need it for Gymnasium-Robotics/MaMuJoCo to support 3.11

Pitch

No response

Alternatives

No response

Additional context

No response

Checklist

[X] I have checked that there is no similar issue in the repo

enhancement
opened by Kallinteris-Andreas 1
[Proposal] Improve AssertOutOfBoundWrapper to support MultiDiscrete spaces
Proposal

When using this wrapper in a piece of code, I noticed that it only works for Discrete spaces from gym. However, extending it to MultiDiscrete spaces too is extremely straightforward: everything needed is to add a case to the assert check on line 11 in assert_out_of_bounds.py:

`

assert all( (isinstance(self.action_space(agent), Discrete) or isinstance(self.action_space(agent), MultiDiscrete)) for agent in getattr(self, "possible_agents", []) ), "should only use AssertOutOfBoundsWrapper for Discrete spaces"

`

MultiDiscrete spaces has the same contains() method of Discrete ones, used in the step() method, so no further adjustment would be needed.

Motivation

MultiDiscrete spaces are supported by many stable-baselines algorithms, and are useful in many situations.

Pitch

The wrapper is useful in many situation, and having it to work with this kind of space as well would be really good.

Alternatives

No response

Additional context

No response

Checklist

[X] I have checked that there is no similar issue in the repo

enhancement
opened by opocaj92 0

Releases(1.22.3)

1.22.3(Dec 28, 2022)
What's Changed

Waterworld_v4: Fixed incorrect pursuer being selected when adding rewards by @TheMikeste1 in https://github.com/Farama-Foundation/PettingZoo/pull/855

Remove AEC diagrams from environments pages by @mgoulao in https://github.com/Farama-Foundation/PettingZoo/pull/856

Add information about MAgent2 standalone package by @dsctt in https://github.com/Farama-Foundation/PettingZoo/pull/857

Switch flake8 from gitlab to github by @RedTachyon in https://github.com/Farama-Foundation/PettingZoo/pull/858

workflow fix by @WillDudley in https://github.com/Farama-Foundation/PettingZoo/pull/867

Versioning by @mgoulao in https://github.com/Farama-Foundation/PettingZoo/pull/865

Bump pillow from 9.2.0 to 9.3.0 in /tutorials/Ray by @dependabot in https://github.com/Farama-Foundation/PettingZoo/pull/859

https://github.com/Farama-Foundation/PettingZoo/security/dependabot/3 by @WillDudley in https://github.com/Farama-Foundation/PettingZoo/pull/869

wrappers documentation: get to_parallel from utils.conversions by @AndrewRWilliams in https://github.com/Farama-Foundation/PettingZoo/pull/870

support Python 3.11 by @WillDudley in https://github.com/Farama-Foundation/PettingZoo/pull/872

New Contributors

@TheMikeste1 made their first contribution in https://github.com/Farama-Foundation/PettingZoo/pull/855

@dependabot made their first contribution in https://github.com/Farama-Foundation/PettingZoo/pull/859

@AndrewRWilliams made their first contribution in https://github.com/Farama-Foundation/PettingZoo/pull/870

Full Changelog: https://github.com/Farama-Foundation/PettingZoo/compare/1.22.2...1.22.3
Source code(tar.gz)
Source code(zip)
1.22.2(Nov 11, 2022)
What's Changed

Fixing for Issue #840 by @BolunDai0216 in https://github.com/Farama-Foundation/PettingZoo/pull/841

fix #845 by @WillDudley in https://github.com/Farama-Foundation/PettingZoo/pull/846

added GitHub Issue Forms as proposed in #844 by @tobirohrer in https://github.com/Farama-Foundation/PettingZoo/pull/848

Changed reward logic in Waterworld by @BolunDai0216 in https://github.com/Farama-Foundation/PettingZoo/pull/843

ENH: add gui to chess by @younik in https://github.com/Farama-Foundation/PettingZoo/pull/842

change paradigm of parallel api loops by @WillDudley in https://github.com/Farama-Foundation/PettingZoo/pull/847

Remove magent docs artifacts by @dsctt in https://github.com/Farama-Foundation/PettingZoo/pull/850

tianshou tuts fixed by @WillDudley in https://github.com/Farama-Foundation/PettingZoo/pull/852

Overhaul env creation guide by @WillDudley in https://github.com/Farama-Foundation/PettingZoo/pull/838

Fix: set render_mode in tianshou tutorials (#853) by @RaffaeleGalliera in https://github.com/Farama-Foundation/PettingZoo/pull/854

New Contributors

@tobirohrer made their first contribution in https://github.com/Farama-Foundation/PettingZoo/pull/848

@RaffaeleGalliera made their first contribution in https://github.com/Farama-Foundation/PettingZoo/pull/854

Full Changelog: https://github.com/Farama-Foundation/PettingZoo/compare/1.22.1...1.22.2
Source code(tar.gz)
Source code(zip)
1.22.1(Oct 25, 2022)
What's Changed

Docs Update 2 by @mgoulao in https://github.com/Farama-Foundation/PettingZoo/pull/817

Remove average total reward from environments pages by @mgoulao in https://github.com/Farama-Foundation/PettingZoo/pull/821

Rename core to AEC by @mgoulao in https://github.com/Farama-Foundation/PettingZoo/pull/822

Add Google Analytics tag by @mgoulao in https://github.com/Farama-Foundation/PettingZoo/pull/825

Remove MAgent content by @dsctt in https://github.com/Farama-Foundation/PettingZoo/pull/823

This updates the setup and other things by @jjshoots in https://github.com/Farama-Foundation/PettingZoo/pull/829

Wd/tutorials ci by @WillDudley in https://github.com/Farama-Foundation/PettingZoo/pull/831

update logo, favicon, and fix broken links by @mgoulao in https://github.com/Farama-Foundation/PettingZoo/pull/832

Wd/tutorials ci by @WillDudley in https://github.com/Farama-Foundation/PettingZoo/pull/833

Bug fixes for Issue #818 by @BolunDai0216 in https://github.com/Farama-Foundation/PettingZoo/pull/836

Update docs by @mgoulao in https://github.com/Farama-Foundation/PettingZoo/pull/837

Fix ref to Gymnasium by @dsctt in https://github.com/Farama-Foundation/PettingZoo/pull/839

Full Changelog: https://github.com/Farama-Foundation/PettingZoo/compare/1.22.0...1.22.1
Source code(tar.gz)
Source code(zip)
1.22.0(Oct 7, 2022)
Major API change: done -> termination and truncation, matching Gymnasium's new API. The dependency gym has been switched with gymnasium, which is maintained.

What's Changed

replace 'done' with 'termination, truncation' new logic by @5cat in https://github.com/Farama-Foundation/PettingZoo/pull/802

Update new Render API by @younik in https://github.com/Farama-Foundation/PettingZoo/pull/800

fix wrapper unwrapped thing by @jjshoots in https://github.com/Farama-Foundation/PettingZoo/pull/808

Update Sphinx theme by @mgoulao in https://github.com/Farama-Foundation/PettingZoo/pull/804

Update security permissions for GitHub workflows by @andrewtanJS in https://github.com/Farama-Foundation/PettingZoo/pull/809

Updated Waterworld by @BolunDai0216 in https://github.com/Farama-Foundation/PettingZoo/pull/807

Docs API Update by @WillDudley in https://github.com/Farama-Foundation/PettingZoo/pull/810

Gymnasium dep by @WillDudley in https://github.com/Farama-Foundation/PettingZoo/pull/814

New Contributors

@5cat made their first contribution in https://github.com/Farama-Foundation/PettingZoo/pull/802

@younik made their first contribution in https://github.com/Farama-Foundation/PettingZoo/pull/800

Full Changelog: https://github.com/Farama-Foundation/PettingZoo/compare/1.21.0...1.22.0
Source code(tar.gz)
Source code(zip)
1.21.0(Sep 24, 2022)
What's Changed

As part of the Gym update to 0.26, the following change has been made:

done -> termination and truncation: The singular done signal has been changed to a termination and truncation signal, where termination dictates that the environment has ended due to meeting certain conditions, and truncation dictates that the environment has ended due to exceeding the time/frame limit.

Butterfly/Prospector, Classic/Mahjong, Classic/Doudizhu, Classic/Backgammon, Classic/Checkers has been pulled.

Some QOL improvements for development, such as moving pyright to pre-commit and enforcing pydocstyle.

Massive website upgrade.

List of Changes

Fix concatvecenvs to work in proper process by @jjshoots in https://github.com/Farama-Foundation/PettingZoo/pull/763

Fix RPS render issue by @jjshoots in https://github.com/Farama-Foundation/PettingZoo/pull/776

Reduces the number of warnings by @pseudo-rnd-thoughts in https://github.com/Farama-Foundation/PettingZoo/pull/777

Pull Prospector by @jjshoots in https://github.com/Farama-Foundation/PettingZoo/pull/779

Remove a bunch of envs by @jjshoots in https://github.com/Farama-Foundation/PettingZoo/pull/781

Potentially fix 13 lines so it doesn't take >6 hours to run by @jjshoots in https://github.com/Farama-Foundation/PettingZoo/pull/782

Move Pyright to pre-commit + add pydocstyle by @kir0ul in https://github.com/Farama-Foundation/PettingZoo/pull/737

Update pre-commit config by @pseudo-rnd-thoughts in https://github.com/Farama-Foundation/PettingZoo/pull/787

Update docs website to Sphinx by @mgoulao in https://github.com/Farama-Foundation/PettingZoo/pull/780

Update links to the new domain by @mgoulao in https://github.com/Farama-Foundation/PettingZoo/pull/789

Truncation Update by @jjshoots in https://github.com/Farama-Foundation/PettingZoo/pull/767

Update Gym version by @WillDudley in https://github.com/Farama-Foundation/PettingZoo/pull/788

More truncation fixes by @jjshoots in https://github.com/Farama-Foundation/PettingZoo/pull/796

Update simple_env.py by @FilipinoGambino in https://github.com/Farama-Foundation/PettingZoo/pull/798

Docs automation by @mgoulao in https://github.com/Farama-Foundation/PettingZoo/pull/790

New Contributors

@pseudo-rnd-thoughts made their first contribution in https://github.com/Farama-Foundation/PettingZoo/pull/777

@mgoulao made their first contribution in https://github.com/Farama-Foundation/PettingZoo/pull/780

Full Changelog: https://github.com/Farama-Foundation/PettingZoo/compare/1.20.1...1.21.0
Source code(tar.gz)
Source code(zip)
1.20.1(Aug 7, 2022)

Refer to the previous version (1.20.0) for list of changes, this version simply exists due to technical problems with publishing to PyPI.
Source code(tar.gz)
Source code(zip)
1.20.0(Aug 7, 2022)
What's Changed

Black to pass master CI by @jjshoots in https://github.com/Farama-Foundation/PettingZoo/pull/734

Refactor Pytest multiple calls to a single call by @kir0ul in https://github.com/Farama-Foundation/PettingZoo/pull/731

Change MPE rendering to use Pygame instead of pyglet #732 by @WillDudley in https://github.com/Farama-Foundation/PettingZoo/pull/738

Remove pyglet and buggy dependency in Multiwalker by @jjshoots in https://github.com/Farama-Foundation/PettingZoo/pull/739

Add EzPickle to MPE by @jjshoots in https://github.com/Farama-Foundation/PettingZoo/pull/741

Fix incorrect observation dict key in documentation by @bkrl in https://github.com/Farama-Foundation/PettingZoo/pull/743

Temporarily remove waterworld from tests and also disable environment. by @jjshoots in https://github.com/Farama-Foundation/PettingZoo/pull/749

Fix broken atari environment path by @manu-hoffmann in https://github.com/Farama-Foundation/PettingZoo/pull/747

Complete Atari Envs path fix by @jjshoots in https://github.com/Farama-Foundation/PettingZoo/pull/750

Correct MPE requirements to reflect PR #738 by @WillDudley in https://github.com/Farama-Foundation/PettingZoo/pull/748

New Contributors

@WillDudley made their first contribution in https://github.com/Farama-Foundation/PettingZoo/pull/738

@bkrl made their first contribution in https://github.com/Farama-Foundation/PettingZoo/pull/743

@manu-hoffmann made their first contribution in https://github.com/Farama-Foundation/PettingZoo/pull/747

Full Changelog: https://github.com/Farama-Foundation/PettingZoo/compare/1.19.1...1.20.0
Source code(tar.gz)
Source code(zip)
1.19.1(Jun 21, 2022)
Update Gym requirement version

Source code(tar.gz)
Source code(zip)
1.19.0(Jun 21, 2022)
Streamlined all envs to have consistent folder structures

Added info as output of reset to be consistent with new Gym API

Source code(tar.gz)
Source code(zip)
0.18.1(Apr 29, 2022)
Massive overhaul to Knight Archers Zombies, version bumped

Changed Atari games to use minimal observation space by default, all versions bumped

Large bug fix to all MAgent environments, versions bumped

MAgent environments now have Windows binaries

Removed Prison environment

Multiwalker bug fix, version bumped

Large number of test fixes

Removed manual_control with new manual_policy method

Converted seed method to argument to reset to match new Gym API

(The PettingZoo 1.18.0 release never existed due to technical issues)
Source code(tar.gz)
Source code(zip)
1.17.0(Mar 15, 2022)
Changed metadata naming scheme to match gym. In particular render.modes -> render_modes and video.frames_per_second -> render_fps

Fixed bad pettingzoo import error messages caused by autodeprication logic

Source code(tar.gz)
Source code(zip)
1.16.0(Mar 5, 2022)
KAZ: Code rewrite and numerous fixes, added manual control capabililty

Supports changes to seeding in gym 0.22.0

Fixed prison state space, bumped version

Fixed battlefield state space

Increased default cycles in api tests (making them catch more errors than before)

Added turn-based to parallel wrapper

Moved magent render logic to Magent repo

Source code(tar.gz)
Source code(zip)
1.15.0(Jan 28, 2022)
Bug fixes to KAZ, pistonball, multiwalker, cooperative pong. Versions bumped.

Removed logo from gather, version bumped.

Added FPS attribute to all environments to make rendering easier.

Multiwalker now uses pygame instead of pyglet for rendering

Renamed to_parallel and from_parallel to aec_to_parallel and parallel_to_aec

Added is_parallelizable metadata to ensure that the aec_to_parallel wrapper is not misused

Fixed the API tests to better support agent generation

Source code(tar.gz)
Source code(zip)
1.14.0(Dec 5, 2021)

-Bug fixes and partial redesign to pursuit environment logic and rendering. Environment is now learnable, version bumped -Bug fixes and reward function redesign for cooperative pong environment, version bumped -Ball moving into the left column due to physics engine imprecision in pistonball no longer gives additional reward, version bumped -PyGame version bumped, no environment version bumps needed -Python 3.10 support -Fixed parallel API tests to allow environments without possible_agents
Source code(tar.gz)
Source code(zip)
1.13.1(Oct 19, 2021)
Fixed unnecessary warnings generated about observation and action spaces

Upstreamed new rlcard version with new texas holdem no limit implementation, bumped version to v6

Updated python chess dependency, bumped version to v5

Dropped support for python 3.6, added official support for 3.9

Various documentation fixes

Source code(tar.gz)
Source code(zip)
1.12.0(Oct 8, 2021)
API changes

new observation_space(agent), action_space(agent) methods that retreive the static space for an agent

possible_agents, observation_spaces, action_spaces attributes made optional. Wrappers pass these attributes through if they exist.

parallel environment's agents list contains agents to take next step, instead of agents that took previous step.

Generated agents now allowed, agents can be created any time during an episode. Note that agents cannot resurect, once they are done, they cannot be readded to the environment.

Fixed unexpected behavior with close method in pursuit environment

Removed pygame loading messages

Fix pillow dependency issue

Removed local ratio arg from pistonball environment

Gym 0.21.0 support

Better code formatting (isort, etc.)

Source code(tar.gz)
Source code(zip)
1.11.1(Aug 19, 2021)
Fix scipy and pyglet dependencies for sisl environments

Fix pistonball rendering (no version bumps)

Update rlcard to v1.0.4 with a fix for texas hold'em no limit; bump version

Source code(tar.gz)
Source code(zip)
1.11.0(Aug 2, 2021)

-Upgraded to RLCard 1.0.3, bumped all versions. Also added support for num_players in RLcard based environments which can have variable numbers of players. -Fixed Go and Chess observation spaces, bumped versions -Minor Go rendering fix -Fix PyGame dependency in classic (used for rendering) -Fixed images being loaded into slow PyGame data structures, resulting in substantial speedups in certain Butterfly games (no version bump needed) -Fix odd cache problem using RGB rendering in cooperative pong -Misc fixes to tests and warning messages
Source code(tar.gz)
Source code(zip)
1.10.0(Jul 17, 2021)
Added continuous action support for MPE environments as an argument

Added pixel art rendering for Texas Hold'em No Limit, Rock Paper Scissors and Go

Fixed pixel art rendering in Connect Four

Fixed bug in order of black/white pieces in Go observation space, bumped version

Changed observation in cooperative pong to include entire screen, bumped version

Source code(tar.gz)
Source code(zip)
1.9.0(Jun 12, 2021)
Created no action timer for pong to encourage players to serve (before there was no penalty to stalling the game forever). Bumped version of all pong environments (pong, basketball_pong, volleyball_pong, foozpong, quadrapong)

Fixed Multiwalker collision bug, bumped version

Add state method to Magent and MPE

Merged rock paper scissors and rock paper scissors lizard spock into a single environment that takes the number of actions as an argument, and adds the n_cycles argument to allow for a single game to be sequential. Bumped version

Removed depricated env_done method

Fixed order of channels in combined_arms observation

Added pixel art based RGB rendering to connect four. This will also be added to rock paper scissors, Go and Texas Holdem in upcoming releases

Moved pettingzoo CI test files outside of the repo

Changed max cycles test to be more robust under agent death

Source code(tar.gz)
Source code(zip)
1.8.2(May 14, 2021)
fixed multiwalker bug, bumped environment version.

Added support for custom render modes in render_test

Source code(tar.gz)
Source code(zip)
1.8.1(Apr 16, 2021)
Added argument to seed test to disable the seed()-reset() test. Docs updated.

Minor changes to MAgent rendering

Source code(tar.gz)
Source code(zip)
1.8.0(Apr 4, 2021)
Fixed arbitrary calls to observe() in classic games (especially tictactoe and connect 4)

Fixed documentation for tictactoe and pistonball

Source code(tar.gz)
Source code(zip)
1.7.0(Mar 27, 2021)
Fixed MAgent bugs and changed default to non-minimap mode, bumped versions.

Fix transient installation error.

Source code(tar.gz)
Source code(zip)
1.6.1(Mar 8, 2021)
Minor miscellaneous fixes and small feature additions:

Added .unwrapped

Minor fix to from_parallel

removed warning from close()

fixed random demo

fixed prison manual control

Source code(tar.gz)
Source code(zip)
1.6.0(Feb 21, 2021)
Changed default values of max_cycles in pistonball, prison, prospector

Changed pistonball default mode to continuous and changed default value for local_ratio

Refactored externally facing tests and utils

Bumped pymunk version to 6.0.0 and bumped versions of all environments which depend on pymunk

Added state() and state_space to API, implemented methods in butterfly environments

Various small bug fixes in butterfly environments.

Documentation updates.

Source code(tar.gz)
Source code(zip)
1.5.2(Jan 29, 2021)

Fix miscellaneous annoying loading messages for butterfly environments. Improvements to save_obs and related functionality. Fixes to KAZ.
Source code(tar.gz)
Source code(zip)
1.5.1(Jan 13, 2021)

Fixes MPE rendering dependency, fixes minor left over dependencies on six, fixes issues when pickling Pistonball. No versions were bumped.
Source code(tar.gz)
Source code(zip)
1.5.0(Jan 5, 2021)

Refactors tests to be generally usable by third party environments. Added average reward calculating util, and made minor improvements to random_demo and save_obs utils. Removed black death argument from KAZ (it's now a wrapper in supersuit). Redid how illegal actions are handled in classic, by making observations dictionaries where one element is the observation and the other is a proper illegal action mask. Pistonball was refactored for readability, to run faster and to allow the number of pistons to be varied via argument. Waterworld was completely refactored with various major fixes. RLCard version was bumped (and includes bug fixes impacting environments). MAgent rendering looks much better now (versions not bumped). Major bug in the observation space of pursuit is fixed. Add Python 3.9 support. Update Gym version. Fixed multiwalker observation space, for good this time, and made large improvements to code quality. Removed NaN wrapper.
Source code(tar.gz)
Source code(zip)
1.4.2(Nov 26, 2020)

Pistonball reward and miscellaneous problems. Fixed KAZ observation and rendering issues. Fix Cooperative Pong issues with rendering. Fixed default parameters in Hanabi. Fixed multiwalker rewards, added arguments. Changed combined_arms observation and rewards, tiger_deer rewards. Added more arguments to all MAgent environments.
Source code(tar.gz)
Source code(zip)