Max van Dijck 232c331ce3 [RLlib] Rename all np.product usage to np.prod (#46317) | 3 月之前 | |
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examples | 331c5b7e13 [RLlib-contrib] Alpha Zero. (#36736) | 1 年之前 |
src | 331c5b7e13 [RLlib-contrib] Alpha Zero. (#36736) | 1 年之前 |
tests | 331c5b7e13 [RLlib-contrib] Alpha Zero. (#36736) | 1 年之前 |
tuned_examples | a9ac55d4f2 [RLlib; RLlib contrib] Move `tuned_examples` into rllib_contrib and remove CI learning tests for contrib algos. (#40444) | 1 年之前 |
BUILD | a9ac55d4f2 [RLlib; RLlib contrib] Move `tuned_examples` into rllib_contrib and remove CI learning tests for contrib algos. (#40444) | 1 年之前 |
README.md | 331c5b7e13 [RLlib-contrib] Alpha Zero. (#36736) | 1 年之前 |
pyproject.toml | 232c331ce3 [RLlib] Rename all np.product usage to np.prod (#46317) | 3 月之前 |
requirements.txt | 232c331ce3 [RLlib] Rename all np.product usage to np.prod (#46317) | 3 月之前 |
Alpha Zero is a general reinforcement learning approach that achieved superhuman performance in the games of chess, shogi, and Go through tabula rasa learning from games of self-play, surpassing previous state-of-the-art programs that relied on handcrafted evaluation functions and domain-specific adaptations.
conda create -n rllib-alpha-zero python=3.10
conda activate rllib-alpha-zero
pip install -r requirements.txt
pip install -e '.[development]'
[AlphaZero Example]()