一些簡單的遊戲,用來驗證強化學習中使用動作遮罩的效果及影響
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Updated
Jul 22, 2021 - Python
一些簡單的遊戲,用來驗證強化學習中使用動作遮罩的效果及影響
Implementation of a multiprocessing Proximal Policy Optimization (PPO) algorithm on the BidepalWalker OpenAI Gym environment.
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This module looks at policy based methods of reinforcement learning, principally the drawbacks to value based methods like Q learning that motivate the use of policy gradients.
Result - Simple monad solution based on C++17 and policy based design
Policy based Reinforcement Learning techniques with REINFORCE and Actor Critic, applied to OpenAI's gym environments.
This repo implements the REINFORCE algorithm for solving the Cart Pole V1 environment of the Gymnasium library using Python 3.8 and PyTorch 2.0.1.
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