A curated list of Monte Carlo tree search papers with implementations.

🧠🔗 From idea to production in just few lines: Graph-Based Programmable Neuro-Symbolic LM Framework - a production-first LM framework built with decade old Deep Learning best practices

Modular framework for Reinforcement Learning in python

Trading environnement for RL agents, backtesting and training.

Implementation of the paper "Overcoming Exploration in Reinforcement Learning with Demonstrations" Nair et al. over the HER baselines from OpenAI

EvoRL is a fully GPU-accelerated framework for Evolutionary Reinforcement Learning, implemented with JAX. It supports Reinforcement Learning (RL), Evolutionary Computation (EC), Evolution-guided Reinforcement Learning (ERL), AutoRL, and seamless integration with GPU-optimized simulation environments.

🚀一个结合了LSTM股票价格预测与强化学习交易策略的智能股票交易系统。通过深度学习对股市数据进行精准预测，并利用强化学习自动优化交易决策，实现了从数据获取、趋势预测到自动交易的全流程智能化。系统不仅提供了强大的数据处理和预测功能，还内置交互式可视化界面，帮助用户实时查看预测结果与交易决策，适用于多支股票的批量处理，帮助投资者更好地捕捉市场机会，提升交易效率与收益。

⚙️ Solving sudoku using Deep Reinforcement learning in combination with powerful symbolic representations.

ReinforceUI-Studio. A Python-based application designed to simplify the configuration and monitoring of RL training processes. Supporting MuJoCo, OpenAI Gymnasium, and DeepMind Control Suite. Algorithms included: CTD4, DDPG, DQN, PPO, SAC, TD3, TQC

Variational Quantum Circuits for Deep Reinforcement Learning since 2019. Xanadu Quantum Software Competition 1st Prize 2019.

💥💥 This is a easy installable extension for OpenAi Gym Environment. This simulates SpaceX Falcon landing.

Learning 2-opt Heuristics for the TSP via Deep Reinforcement Learning

🐍 🏋 OpenAI GYM for Nintendo NES emulator FCEUX and 1983 game Mario Bros. + Double Q Learning for mastering the game

A well-documented A2C written in PyTorch

PyTorch implementation of DDPG for continuous control tasks.

A reusable framework for successor features for transfer in deep reinforcement learning using keras.

Tabular methods for reinforcement learning

Reinforced Causal Explainer for Graph Neural Networks, TPAMI2022

Implementation of algorithmic trading using reinforcement learning.

A PPO agent leveraging reinforcement learning performs Penetration Testing in a simulated computer network environment. The agent is trained to scan for vulnerabilities in the network and exploit them to gain access to various network resources.