Practical RL

Features

Deep Reinforcement Learning Implementations - Provides functional implementations of advanced reinforcement learning agents using deep neural networks.
Reinforcement Learning Implementations - Implements a wide range of reinforcement learning agents, including value-based, model-free, and policy-gradient algorithms.
Experience Replay Buffers - Implements memory structures that store agent transitions to break temporal correlations during neural network training.
Expert Imitation Learning - Implements frameworks for training agents to mimic expert demonstrations using inverse reinforcement learning and behavior cloning.
Policy and Value Function Approximators - Constructs neural network architectures specifically to estimate values and action probabilities in reinforcement learning.
Policy Gradient Methods - Provides gradient-based architectures for updating policy parameters in discrete and continuous action spaces.
Partially Observable MDP Solvers - Provides architectures using recurrent neural networks to solve decision processes in environments with hidden state information.
Deep Learning Foundations - Provides core frameworks and educational resources for machine learning and neural networks.
Reinforcement Learning Curricula - Provides structured learning paths specifically for the study of reinforcement learning.
Behavioral Agent Training Environments - Provides a training environment for solving complex tasks through trial and error across robotics and finance domains.
Imitation Learning Guides - Provides materials for developing behavioral policies based on expert demonstrations and inverse reinforcement learning techniques.
Exploration Strategies - Provides tools and implementations for balancing exploration and exploitation using methods like Thompson Sampling and Monte Carlo Tree Search.
Model-Based Reinforcement Learning Frameworks - Provides a guide to designing exploration strategies and planners using Monte Carlo Tree Search and Thompson Sampling.
Monte Carlo Tree Search - Implements a search algorithm that uses random sampling of the game tree to determine optimal moves.
Recurrent Neural Network Training - Provides frameworks for building and training recurrent networks with parallelized computational efficiencies.
Memory Architectures for Partially Observable MDPs - Integrates hidden state tracking via recurrent layers to handle partially observable environments and temporal dependencies.
Target Network Decoupling - Uses a separate, slowly updating set of weights to prevent divergence during value function approximation.

Open-source alternatives to Practical RL

Similar open-source projects, ranked by how many features they share with Practical RL.

morvanzhou/pytorch-tutorial
MorvanZhou/PyTorch-Tutorial
8,458View on GitHub
This project is a collection of PyTorch learning resources and educational guides designed to teach the construction and training of neural networks. It serves as a comprehensive deep learning tutorial covering various model architectures and practical implementation strategies. The resources provide specific guidance on implementing computer vision tasks, such as image classification and synthetic imagery generation, as well as reinforcement learning agents using value networks and experience replay. It also covers sequential data modeling through recurrent networks and generative modeling u
Jupyter Notebookautoencoderbatchbatch-normalization
View on GitHub8,458
morvanzhou/reinforcement-learning-with-tensorflow
MorvanZhou/Reinforcement-learning-with-tensorflow
9,464View on GitHub
This project is an educational repository of reinforcement learning agents and tutorials implemented using TensorFlow. It provides a practical codebase for both model-free and model-based learning agents, designed to demonstrate how AI agents learn through trial and error. The collection features detailed implementations of various algorithmic approaches, including Deep Q-Networks and Policy Gradient methods. It specifically covers Actor-Critic architectures for continuous and discrete action spaces, alongside Proximal Policy Optimization and Deep Deterministic Policy Gradients. The framewor
Pythona3cactor-criticasynchronous-advantage-actor-critic
View on GitHub9,464
wangshusen/drl
wangshusen/DRL
4,512View on GitHub
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 curriculum extends beyond these core topics to include imitation learning, multi-agent training, and methods for handling continuous action spaces. Content is organized as markdown-driven documentation that generates static, navig
View on GitHub4,512
tensorlayer/tensorlayer
tensorlayer/TensorLayer
7,384View on GitHub
TensorLayer is a backend-agnostic tensor library and deep learning framework designed for building neural network architectures. It provides a neural network abstraction layer that allows model logic to run across different deep learning engines using high-level layers and model components. The project serves as a deep reinforcement learning toolkit for implementing policy-based, value-based, and actor-critic agents. It includes specialized tools for managing experience replay and gradient-based policy optimization to handle both discrete and continuous action spaces. To support reinforcemen
Python
View on GitHub7,384

See all 30 alternatives to Practical RL

yandexdataschoolPractical_RL

Features

Open-source alternatives to Practical RL

MorvanZhou/PyTorch-Tutorial

MorvanZhou/Reinforcement-learning-with-tensorflow

wangshusen/DRL

tensorlayer/TensorLayer

Star history

Open-source alternatives to Practical RL

MorvanZhou/PyTorch-Tutorial

MorvanZhou/Reinforcement-learning-with-tensorflow

wangshusen/DRL

tensorlayer/TensorLayer