JORLDY
muzero-general
| JORLDY | muzero-general | |
|---|---|---|
| 1 | 14 | |
| 356 | 2,382 | |
| 0.0% | - | |
| 0.0 | 0.0 | |
| about 1 year ago | 4 months ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
JORLDY
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JORLDY: OpenSource Reinforcement Learning Framework
JORLDY github link: https://github.com/kakaoenterprise/JORLDY
muzero-general
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Open source rules engine for Magic: The Gathering
I went looking for MuZero implementations in order to see how, exactly, they interact with the game space. Based on this one, which had the most stars in the muzero topic, it appears that it needs to be able to discern legal next steps from the current game state https://github.com/werner-duvaud/muzero-general/blob/master/...
So, I guess for the cards Forge has implemented one could MuZero it, but I believe it's a bit chicken and egg with a "free text" game like M:TG -- in order to train one would need to know legal steps for any random game state, but in order to have legal steps one would need to be able to read and interpret English rules and card text
- I placed Stockfish (white) against ChatGPT (black). Here's how the game went.
- Ask HN: What interesting problems are you working on? ( 2022 Edition)
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How to "fit" the output of the Critic to the dimension of the reward?
You may want to use the trick described in https://arxiv.org/pdf/1805.11593.pdf as a Transformed Bellman Operator. Its efficiency is proved in MuZero original paper https://arxiv.org/pdf/1911.08265.pdf Appendix F. The implementation of that method you can find here: https://github.com/werner-duvaud/muzero-general Usage: muzero/models.py:649 (def support_to_scalar)
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MuZero unable to solve non-slippery FrozenLake environment?
I have used this implementation from MuZero: https://github.com/werner-duvaud/muzero-general
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RL for chess
+1 to taking a look at OpenSpiel. It has AlphaZero in C++ and Python, and there is even a PR open that allows running UCI (e.g. Stockfish) bot. You can also load chess via the OpenSpiel wrapper in muzero-general: https://github.com/werner-duvaud/muzero-general
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The future of MuZero, and where to go for news
When I looked up some community implementations, like that of Werner Duvaud on GitHub and Discord, hoping to make my own contributions to this effect, I soon found that I was hopelessly out of my depth as an amateur programmer, even with the help of some other sources like this walkthrough series. However, from what I could tell, most of the people working on this sort of thing seemed to be tackling relatively simple games. At first I thought this might be largely due to limitations in hobby time or computing power available to these users, but then I also noticed that, unless I have misunderstood something, it seems like the games are required to be rebuilt entirely in the engine of (this implementation of) MuZero, which would also obviously be a limit on the complexity of games chosen.
- Is MuZero currently the best RL algo that we have now?
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"muzero-general", PyTorch/Ray code for Gym/Atari/board-games (reasonable results + checkpoints for small tasks)
Windows support (Experimental / Workaround: Use the notebook in Google Colab)
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Muzero code implementation
There are several if you google "muzero github", e.g. https://github.com/werner-duvaud/muzero-general
What are some alternatives?
LightZero - [NeurIPS 2023 Spotlight] LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios
deep-RL-trading - playing idealized trading games with deep reinforcement learning
Super-mario-bros-PPO-pytorch - Proximal Policy Optimization (PPO) algorithm for Super Mario Bros
AgileRL - Streamlining reinforcement learning with RLOps. State-of-the-art RL algorithms and tools.
alpha-zero-general - A clean implementation based on AlphaZero for any game in any framework + tutorial + Othello/Gobang/TicTacToe/Connect4 and more
cleanrl - High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
open_spiel - OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games.
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
stable-baselines3-contrib - Contrib package for Stable-Baselines3 - Experimental reinforcement learning (RL) code
pytorch-ddpg - Deep deterministic policy gradient (DDPG) in PyTorch 🚀
seed_rl - SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference. Implements IMPALA and R2D2 algorithms in TF2 with SEED's architecture.