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)
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Updated
Oct 23, 2024 - Python
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)
Master classic RL, deep RL, distributional RL, inverse RL, and more using OpenAI Gym and TensorFlow with extensive Math
A pytorch tutorial for DRL(Deep Reinforcement Learning)
Deep Reinforcement Learning codes for study. Currently, there are only codes for algorithms: DQN, C51, QR-DQN, IQN, QUOTA.
C51-DDQN in Keras
DQN-Atari-Agents: Modularized & Parallel PyTorch implementation of several DQN Agents, i.a. DDQN, Dueling DQN, Noisy DQN, C51, Rainbow, and DRQN
Paddle-RLBooks is a reinforcement learning code study guide based on pure PaddlePaddle.
A collection of Deep Reinforcement Learning algorithms implemented with PyTorch to solve Atari games and classic control tasks like CartPole, LunarLander, and MountainCar.
🐳 Implementation of various Distributional Reinforcement Learning Algorithms using TensorFlow2.
🍰 51单片机实验
An implementation of an Autonomous Vehicle Agent in CARLA simulator, using TF-Agents
PyTorch - Implicit Quantile Networks - Quantile Regression - C51
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