使用Transfer Learning:例如,使用预训练的神经网络来提高算法的数据有限情况下的性能。 使用Reinforcement Learning from Demonstrations(RLFD):例如,使用人工演示来帮助强化学习算法学习。 使用Multi-Agent Reinforcement Learning(MARL):例如,使用多个智能体协同工作来解决复杂问题。 6.结论 在
这是我的Github仓库：https://github.com/XinJingHao/Deep-Reinforcement-Learning-Algorithms-with-Pytorch...
You can do that step-by-step in this course on Reinforcement Learning with Gymnasium in Python, where you’ll explore many algorithms including Q-learning, SARSA, and more. Be sure to use the function we’ve just created to animate your agents' progress, and have fun! Conclusion ...
这是我的Github仓库：https://github.com/XinJingHao/Deep-Reinforcement-Learning-Algorithms-with-Pytorch...
Constants.py DecisionMaker.py Exceptions.py LICENSE Logging.py MDPCDModel.py MDPDTModel.py MDPModel.py QDTModel.py QModel.py README.md __init__.py Repository files navigation README MIT license Reinforcement Learning Implementation of Reinforcement Learning algorithms in PythonAbou...
信任域类算法请看:信赖域策略优化(Trust Region Policy Optimization, TRPO),近端策略优化算法(Proximal Policy Optimization Algorithms, PPO),重要性采样(Importance Sampling)——TRPO与PPO的补充 Python实现基于策略函数的强化学习方法:Deep Reinforcement Learning Hands-On——Policy Gradients – an Alternative ...
ShangtongZhang/reinforcement-learning-an-introduction8.OpenAI Gym目前强化学习编程实战常用的环境就是...
TorchRL is an open-source Reinforcement Learning (RL) library for PyTorch. Key features 🐍 Python-first: Designed with Python as the primary language for ease of use and flexibility ⏱️ Efficient: Optimized for performance to support demanding RL research applications 🧮 Modular, customizable...
Monte Carlo is one of the most popular and most commonly used algorithms in various fields ranging from physics and mechanics to computer science. The Monte Carlo algorithm is used inreinforcement learning(RL) when the model of the environment is not known. In the previous chapter, we looked ...
Q-learning is one of the easiest Reinforcement Learning algorithms. The problem with Q-learning however is, once the number of states in the environment are very high, it becomes difficult to implement them with Q table as the size would become very, very large. State of the art techniques...