RL-based agent for playing Atari Pong
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Updated
May 26, 2024 - Python
RL-based agent for playing Atari Pong
An elegant PyTorch deep reinforcement learning library.
This project provides a comprehensive understanding of reinforcement learning, focusing on Deep Q-Learning (DQN). It involves exploring the OpenAI Gym library, implementing DQN from DeepMind's seminal paper, and enhancing the DQN algorithm for improved performance and stability.
Code repository with classical reinforcement learning and deep reinforcement learning methods for Pokémon battles in Pokémon Showdown.
Unofficial implementation of DeepPack in PyTorch. DeepPack is a deep reinforcement learning based algorithm dealing with 2D online bin packing problem.
Modularized Implementation of Deep RL Algorithms in PyTorch
Implementation of the Double Deep Q-Learning algorithm with a prioritized experience replay memory to train an agent to play the minichess variante Gardner Chess
Simple Reinforcement learning tutorials, 莫烦Python 中文AI教学
强化学习中文教程(蘑菇书🍄),在线阅读地址:https://datawhalechina.github.io/easy-rl/
Build and test DRL algorithms in different environments
Clean, Robust, and Unified PyTorch implementation of popular DRL Algorithms (Q-learning, Duel DDQN, PER, C51, Noisy DQN, PPO, DDPG, TD3, SAC, ASL)
Basic code for reinforcement learning and small programs.
gym environnement to simulate the energetic behaviour of a real estate
Double deep q network implementation in OpenAI Gym's "Mountain Car" environment
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A clean framework and implementations for reinforcement learning algorithms.
Double Deep Q-Learning for Optimal Execution implementation
Deep Reinforcement Learning bot built with custom Gym environment and Pygame
Apply Double Deep Q Learning
Apply Double Dueling DQN
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