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This repository provides a comprehensive library of reinforcement learning algorithms designed for training autonomous agents. It serves as a research-oriented collection of implementations that cover fundamental decision-making strategies, including dynamic programming, temporal difference learning, and policy gradient methods. The project distinguishes itself by offering specialized frameworks for deep reinforcement learning and structured decision modeling. It includes implementations for deep Q-learning that utilize neural networks, experience replay, and prioritized sampling to approxima
This project is a collection of reinforcement learning implementations and educational materials written in Python. It provides neural network architectures for solving control tasks through deep reinforcement learning, spanning value-based and policy-gradient methods. The repository includes a library of evolutionary strategies and genetic algorithms as alternatives to gradient-based learning. It also features a model-based system for predicting future environment states and rewards to enable internal simulation and offline planning. The codebase covers a wide range of capabilities, includi
Practical_RL is a comprehensive educational curriculum and course for learning to design and implement agents that solve complex decision processes. It provides a structured study program covering the fundamentals of reinforcement learning, from basic trial-and-error behavior to advanced deep reinforcement learning. The project includes specialized guides and frameworks for imitation learning based on expert demonstrations, model-based reinforcement learning using planners, and the training of recurrent neural networks to solve partially observed environments. The materials cover a broad ran
This project is a deep reinforcement learning curriculum providing educational materials and implementation exercises for mastering neural network-based agents. It serves as a framework for building reference versions of value-based and policy-based methods to solve sequential decision problems. The project provides specific implementations for continuous control simulations and multi-agent reinforcement learning, where agents are trained to cooperate or compete in shared environments. It includes a policy gradient framework for optimizing agent behavior through methods such as REINFORCE. Ca
DRL is a curated educational resource that teaches deep reinforcement learning through a structured series of lectures and videos. It covers the three main families of reinforcement learning methods: actor-critic architectures, value-based algorithms like Q-learning and DQN, and policy-based techniques that directly optimize an agent's action-selection strategy.
The main features of wangshusen/drl are: Reinforcement Learning Curricula, Actor-Critic Architectures, Deep Learning Policy Frameworks, Deep Q-Learning Frameworks, Policy Gradient Methods, Reinforcement Learning Value Estimators, Value-Based Methods Tutorials, Lecture Sequences.
Open-source alternatives to wangshusen/drl include: dennybritz/reinforcement-learning — This repository provides a comprehensive library of reinforcement learning algorithms designed for training autonomous… andri27-ts/reinforcement-learning — This project is a collection of reinforcement learning implementations and educational materials written in Python. It… yandexdataschool/practical_rl — Practical_RL is a comprehensive educational curriculum and course for learning to design and implement agents that… udacity/deep-reinforcement-learning — This project is a deep reinforcement learning curriculum providing educational materials and implementation exercises… packtpublishing/deep-reinforcement-learning-hands-on — This project serves as an educational resource and training framework for developing intelligent agents through deep… morvanzhou/reinforcement-learning-with-tensorflow — This project is an educational repository of reinforcement learning agents and tutorials implemented using TensorFlow.…