open_spiel
pybind11
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open_spiel | pybind11 | |
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44 | 42 | |
3,989 | 14,708 | |
1.2% | 1.5% | |
9.4 | 8.7 | |
4 days ago | 4 days ago | |
C++ | C++ | |
Apache License 2.0 | GNU General Public License v3.0 or later |
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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
pybind11
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Experience using crow as web server
I'm investigating using C++ to build a REST server, and would love to know of people's experiences with Crow-- or whether they would recommend something else as a "medium-level" abstraction C++ web server. As background, I started off experimenting with Python/FastAPI, which is great, but there is too much friction to translate from pybind11-exported C++ objects to the format that FastAPI expects, and, of course, there are inherent performance limitations using Python, which could impact scaling up if the project were to be successful.
- Swig – Connect C/C++ programs with high-level programming languages
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returning numpy arrays via pybind11
I have a C++ function computing a large tensor which I would like to return to Python as a NumPy array via pybind11.
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I created smooth_lines python module, great for drawing software
This is based on the Google Ink Stroke Modeler C++ library, and using pybind11 to make it available on python.
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Facial Landmark Detection with C++
pybind11 makes it easy to call C++ from Python if you want to mix.
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Python’s Multiprocessing Performance Problem
If you've never used Pybind before these pybind tests[1] and this repo[2] have good examples you can crib to get started (in addition to the docs). Once you handle passing/returning/creating the main data types (list, tuple, dict, set, numpy array) the first time, then it's mostly smooth sailing.
Pybind offers a lot of functionality, but core "good parts" I've found useful are (a) use a numpy array in Python and pass it to a C++ method to work on, (b) pass your python data structure to pybind and then do work on it in C++ (some copy overhead), and (c) Make a class/struct in C++ and expose it to Python (so no copying overhead and you can create nice cache-aware structs, etc.).
[1] https://github.com/pybind/pybind11/blob/master/tests/test_py...
- Making Python Web Application with C++ Backend
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Using pybind11 with minGW to cross compile pyhton module for Windows
I have a python module for which the logic is written in C++ and I use pybind11 to expose the objects and functions to Python.
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IPC communication between rust, c++, and python
Reading from Python requires a wrapper, using pybind11 this is fairly done.
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[ADVICE] Python to C++
Also I can highly recommend starting using C++ to augment your Python code, i.e. find the parts that are slow or undoable in Python and write those in C++ then expose them as Python functions. You can use https://github.com/pybind/pybind11 to call C++ code from Python.
What are some alternatives?
muzero-general - MuZero
PyO3 - Rust bindings for the Python interpreter
PettingZoo - An API standard for multi-agent reinforcement learning environments, with popular reference environments and related utilities
nanobind - nanobind: tiny and efficient C++/Python bindings
gym - A toolkit for developing and comparing reinforcement learning algorithms.
Optional Argument in C++ - Named Optional Arguments in C++17
rlcard - Reinforcement Learning / AI Bots in Card (Poker) Games - Blackjack, Leduc, Texas, DouDizhu, Mahjong, UNO.
setuptools-rust - Setuptools plugin for Rust support
gym-battleship - Battleship environment for reinforcement learning tasks
PEGTL - Parsing Expression Grammar Template Library
TexasHoldemSolverJava - A Java implemented Texas holdem and short deck Solver
sol2 - Sol3 (sol2 v3.0) - a C++ <-> Lua API wrapper with advanced features and top notch performance - is here, and it's great! Documentation: