OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games. (by deepmind)

Open_spiel Alternatives

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NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a better open_spiel alternative or higher similarity.

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Reviews and mentions

Posts with mentions or reviews of open_spiel. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-11-14.
  • Need Stochastic Environments
    2 projects | | 14 Nov 2021
    You could try backgammon, blackjack, or poker from OpenSpiel:
  • Any tutorial on how to create RL C++ environments?
    7 projects | | 5 Oct 2021
    It's not exactly a tutorial, but OpenSpiel has C++ environments ported to Python that are relatively simple and easy to understand. Procgen would be a more complicated reference to check out as well.
  • 8+ Reinforcement Learning Project Ideas
    8 projects | | 30 Sep 2021
    8. Build a Chess Bot with OpenSpiel
  • How to deal with non-static state/action space?
    1 project | | 5 Jul 2021
    +1 this is the standard way. If you want to look over an example DQN impl with this for games, see
  • Simple (few states) two-agent environments?
    3 projects | | 25 Jun 2021
    +1 on PettingZoo, and the wrappers they provide as SuperSuit come in handy as well!. Also check out OpenSpiel
  • chess environment
    2 projects | | 24 Jun 2021
    +1 for OpenSpiel if you are concerned about speed because it wraps a C++ implementation. You can expose custom views on the states (i.e. bitmaps, whatever you want) to Python by just adding more functions to the C++ objects and then adding python bindings here: Check out similar files for other games for more examples.
    2 projects | | 24 Jun 2021
    There are a few popular chess environments for RL that you could definitely check out: OpenSpiel ( and PettingZoo (
  • RL for chess
    2 projects | | 5 Jun 2021
    I personally haven’t used it before, but DeepMind have developed a framework called OpenSpiel that provides a Chess environment.
  • Python OpenAI Gym environment for reinforcement learning
    2 projects | | 13 May 2021
    For board games, check out OpenSpiel: It has the largest collection of any I know including the classics.
  • [P] Looking for a teammate in implementing a neat algorithm in Python with C++ as the backend.
    4 projects | | 27 Mar 2021
    Take a look here:
  • Anybody using Go (the programming language) for RL research?
    1 project | | 22 Mar 2021
    Anyway, I maintain OpenSpiel, a framework for RL in Games. We've had a Go API that we've been using internally for some time now and we just released it publicly, details here: It's super bare-bones at the moment but we hope to build it up if there's a community of Go users working on RL that would use it. We'll be adding some basic RL algorithms to it in the coming months.
  • [P] Is anyone considering implementing gym or a similar interface in C++ or Rust?
    2 projects | | 16 Mar 2021
    You should check out OpenSpiel. Its a C++ framework (but also has python bindings), which is a gym-like collection of environments. A lot of the games are card/2-player games, but there are some cooperative/single-agent games as well (I'm the author of the stones_n_games, which is a single player environment). Adding new games is fairly straight forward, and there are some example RL algorithms implemented in both python/C++ which lets you see how to use the framework.
  • Problems making a Tic-Tac-Toe bot
    2 projects | | 28 Feb 2021
    I'm not sure if this is helpful, but there is a reference implementation in OpenSpiel. If you look at python/examples/ and change game = "breakthrough" to game = "tic_tac_toe", and then env_configs = {}, this runs DQN and evaluates against random. After 200k episodes, I'm getting average reward 0.995 for player 1 and around 0.9 for player 2. If you average over seats that's 0.95, but this is in [-1,1] so that's 0.975 in [0,1], Keep in mind that's not really the percentage of wins sin it's not separating wins, draws, and losses-- thought you can easily modify that if you wast-- but the win percentage won't be any higher than that. The code base also has Connect Four so you can try that too. And if you really want the real upper-bound, you can solve the game using value iteration to get the optimal policy and then do expectimax over a random policy versus the optimal greedy policy (or estimate it from many Monte Carlo samples). The win percentage may still rise with more training, but no matter how you slice it, you're not going to get to 100% wins :)
  • Releasing my Texas holdem solver (opensourced)
    3 projects | | 21 Feb 2021
    Hi, thanks:)

    You can find larger versions of Hold'em in the universal_poker game, which wraps the Annual Computer Poker codebase:

    Check out for example uses. It supports limit and common abstractions as well as the full game of no-limit.

    3 projects | | 21 Feb 2021
    this is very cool.

    another interesting open-source project for imperfect-information game solving is DeepMind's OpenSpiel:

    However as far as poker goes, I believe they only provide toy versions of the game, even though theoretically you could put in your own implementation.

    I've noticed that in this part of the AI world, things tend to be closed-source for whatever reason and I think that limits the ability to learn. Seems good to see that changing.


Basic open_spiel repo stats
2 days ago

deepmind/open_spiel is an open source project licensed under Apache License 2.0 which is an OSI approved license.

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