A Torch Based RL Framework for Rapid Prototyping of Research Papers
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Updated
Sep 26, 2024 - Python
A Torch Based RL Framework for Rapid Prototyping of Research Papers
Clean, Robust, and Unified PyTorch implementation of popular Deep Reinforcement Learning (DRL) algorithms (Q-learning, Duel DDQN, PER, C51, Noisy DQN, PPO, DDPG, TD3, SAC, ASL)
强化学习中文教程(蘑菇书🍄),在线阅读地址:https://datawhalechina.github.io/easy-rl/
Exploring deep reinforcement learning techniques, particularly focusing on the implementation and testing of algorithms like Deep Q-Learning (DQN) and Dueling DQN.
This repository compares two methodologies for music recommendation: Q-learning and Deep Reinforcement Learning (Dueling DQN), applied to a dataset of music tracks with features like genre, artist, and danceability. The goal is to build a system that recommends music based on user preferences.
Simple Reinforcement learning tutorials, 莫烦Python 中文AI教学
Build and test DRL algorithms in different environments
Basic code for reinforcement learning and small programs.
🍄Reinforcement Learning: Super Mario Bros with dueling dqn🍄
Optimizing Value-Based Reinforcement Learning Using DQN.
A collection of RL algorithms in PyTorch
Deep Reinforcement Learning with Custom Environment
Reinforcement Learning on ViZDoom Enviroment using Advantage Actor Critic and Dueling Deep Q Networks.
Minimum viable reinforcement learning algorithms for your educational convenience.
A clean framework and implementations for reinforcement learning algorithms.
An epsilon-greedy Dueling Deep Q-Network Based on Prioritised Experience Replay to compute the minimal time path for traversing a maze.
Signal novelty detection as an intrinsic reward for robotics
Reinforcement learning tutorials
Apply Double Dueling DQN
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