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)
A collection of Deep Reinforcement Learning algorithms implemented with PyTorch to solve Atari games and classic control tasks like CartPole, LunarLander, and MountainCar.
Naive implementations of deep reinforcement learning algorithms
An implementation of an Autonomous Vehicle Agent in CARLA simulator, using TF-Agents
Deep Reinforcement Learning codes for study. Currently, there are only codes for algorithms: DQN, C51, QR-DQN, IQN, QUOTA.
Implementation of some of the Deep Distributional Reinforcement Learning Algorithms.
Paddle-RLBooks is a reinforcement learning code study guide based on pure PaddlePaddle.
A TF2.0 implementation of RL baselines.
🐳 Implementation of various Distributional Reinforcement Learning Algorithms using TensorFlow2.
Training Deep RL agents in VizDoom.
A deep reinforcement learning algorithms repo in pytorch
PyTorch - Implicit Quantile Networks - Quantile Regression - C51
C51-DDQN in Keras
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