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
Sep 24, 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)
Deep Reinforcement Learning codes for study. Currently, there are only codes for algorithms: DQN, C51, QR-DQN, IQN, QUOTA.
C51-DDQN in Keras
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.
PyTorch - Implicit Quantile Networks - Quantile Regression - C51
An implementation of an Autonomous Vehicle Agent in CARLA simulator, using TF-Agents
A TF2.0 implementation of RL baselines.
Implementation of some of the Deep Distributional Reinforcement Learning Algorithms.
Naive implementations of deep reinforcement learning algorithms
A deep reinforcement learning algorithms repo in pytorch
Training Deep RL agents in VizDoom.
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