TexasHoldemSolverJava
open_spiel
| TexasHoldemSolverJava | open_spiel | |
|---|---|---|
| 5 | 44 | |
| 774 | 4,004 | |
| - | 0.9% | |
| 0.0 | 9.5 | |
| 10 months ago | 3 days ago | |
| Java | C++ | |
| MIT License | Apache License 2.0 |
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TexasHoldemSolverJava
- OpenSource Solver
- What is the math like behind a typical GTO solver?
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Post-flop (turn / river) solver in browser
It's based on the work open-sourced in https://github.com/bupticybee/TexasHoldemSolverJava, which claimed to be comparable with PioSolver.
- Releasing my opensourced Texas holdem solver with a GUI
- Releasing my Texas holdem solver (opensourced)
open_spiel
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What projects or open-source contributions can impress Jane Street recruiters for a Quant SWE role ?
Deep mind actually has a repository where they applied this algorithm for incomplete-knowledge games. You could use it for reference: https://github.com/deepmind/open_spiel/tree/master/open_spiel/python/algorithms
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I want to build a learning agent for a combinatorial game
+1. You can also find an implementation of Clobber and AlphaZero (and many other basic RL algorithms) in OpenSpiel: https://github.com/deepmind/open_spiel
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minimax for imperfect-information turn-games?
You can find a lot of code online if you look, and many of these applied to Poker. There's a general implementation of both in Python and C++ in OpenSpiel, with some examples applied to small poker games. It's nice code to learn from because the algorithms operate over generic game descriptions, so there aren't game-specific design choices mixed up with the implementation of the algorithms, and you can create your own poker game and just run them on it.
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OpenSpiel 1.3 Released!
And many other additions and improvements. See all the details here: https://github.com/deepmind/open_spiel/releases/tag/v1.3
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What's a good OpenAI Gym Environment for applying centralized multi-agent learning using expected SARSA with tile coding?
I would checkout the openspiel package. It's main focus is RL in games (multi-agent environments). You'll find RL examples there and games that are small enough to solve without deep RL. There's also a wide range of environments from fully cooperative to adversarial zero-sum.
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Competitive reinforcement learning for turn-based games
Hi, you can check out OpenSpiel: https://github.com/deepmind/open_spiel/
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Reinforcement learning and Game Theory a turn-based game
as for algorithms , openspiel repository has few implementations some of these are not related to imperfect information games , and others are not for multiagent environment and others are tabular algorithms .
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Shimmy 1.0: Gymnasium & PettingZoo bindings for popular external RL environments
This includes single-agent Gymnasium wrappers for DM Control, DM Lab, Behavior Suite, Arcade Learning Environment, OpenAI Gym V21 & V26. Multi-agent PettingZoo wrappers support DM Control Soccer, OpenSpiel and Melting Pot. For more information, read the release notes here:
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How to deal with situations where the RL agent cannot act at every time step?
I've had some success using Action Masking - you can refer to here https://github.com/deepmind/open_spiel/blob/120420a74a69354d64c10b51cd129d4587f9f325/open_spiel/python/algorithms/dqn.py but for DQN you need to mask out q values for invalid actions (as well as masking them during prediction). In my case I'm able to place my mask in the observation so can fetch it quite easily during prediction but if that's not possible you could query it from the environment and store it in the replay buffer (like they do in the link I shared)
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How to search the game tree with depth-first search?
Take a look at this simple implementation: https://github.com/deepmind/open_spiel/blob/master/open_spiel/algorithms/minimax.cc
What are some alternatives?
TexasSolver - 🚀 A very efficient Texas Holdem GTO solver :spades::hearts::clubs::diamonds:
muzero-general - MuZero
PokerHandEvaluator - Poker-Hand-Evaluator: An efficient poker hand evaluation algorithm and its implementation, supporting 7-card poker and Omaha poker evaluation
PettingZoo - An API standard for multi-agent reinforcement learning environments, with popular reference environments and related utilities
holdem - A Lightweight Texas Hold'em Poker Game
gym - A toolkit for developing and comparing reinforcement learning algorithms.
Choco - An open-source Java library for Constraint Programming
rlcard - Reinforcement Learning / AI Bots in Card (Poker) Games - Blackjack, Leduc, Texas, DouDizhu, Mahjong, UNO.
OptaPlanner - Java Constraint Solver to solve vehicle routing, employee rostering, task assignment, maintenance scheduling, conference scheduling and other planning problems.
gym-battleship - Battleship environment for reinforcement learning tasks
tensortrade - An open source reinforcement learning framework for training, evaluating, and deploying robust trading agents.
ml-agents - The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents using deep reinforcement learning and imitation learning.