A Torch Based RL Framework for Rapid Prototyping of Research Papers
-
Updated
May 29, 2024 - Python
A Torch Based RL Framework for Rapid Prototyping of Research Papers
This repository implements the use of AI for robot tasks.
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
A PyTorch implementation of the DRR framework (deep reinforcement learning, DDPG, PER, PMF) as it applies to restaurant recommendation.
Autonomous Driving W/ Deep Reinforcement Learning in Lane Keeping - DDQN and SAC with kinematics/birdview-images
Clean, Robust, and Unified PyTorch implementation of popular DRL Algorithms (Q-learning, Duel DDQN, PER, C51, Noisy DQN, PPO, DDPG, TD3, SAC, ASL)
Third homework for the Reinforcement Learning course
Actor Prioritized Experience Replay
gym environnement to simulate the energetic behaviour of a real estate
强化学习算法库,包含了目前主流的强化学习算法(Value based and Policy based)的代码,代码都经过调试并可以运行
Mapless Collision Avoidance of Turtlebot3 Mobile Robot Using DDPG and Prioritized Experience Replay
Implementation code when learning deep reinforcement learning
RLCodebase: PyTorch Codebase For Deep Reinforcement Learning Algorithms
Implementation of project 1 for Udacity's Deep Reinforcement Learning Nanodegree
Prioritized Experience Replay implementation with proportional prioritization
Deep Reinforcement Learning: Value-Based methods. An implementation of DQN, DDQN, Dueling Architectures, DQV, DQV-Max on the PyTorch Lightning framework.
Reinforcement learning algorithm implements.
Reinforcement Learning Agents for Analog Circuit Sizing in Haskell.
PyTorch Implementation of Implicit Quantile Networks (IQN) for Distributional Reinforcement Learning with additional extensions like PER, Noisy layer, N-step bootstrapping, Dueling architecture and parallel env support.
Add a description, image, and links to the prioritized-experience-replay topic page so that developers can more easily learn about it.
To associate your repository with the prioritized-experience-replay topic, visit your repo's landing page and select "manage topics."