Modularized Implementation of Deep RL Algorithms in PyTorch
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Updated
Apr 16, 2024 - Python
Modularized Implementation of Deep RL Algorithms in PyTorch
SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning
A PyTorch implementation of Rainbow DQN agent
TD3, SAC, IQN, Rainbow, PPO, Ape-X and etc. in TF1.x
Customisable Unified Physical Simulations (CUPS) for Reinforcement Learning. Experiments run on the ai2thor environment (http://ai2thor.allenai.org/) e.g. using A3C, RainbowDQN and A3C_GA (Gated Attention multi-modal fusion) for Task-Oriented Language Grounding (tasks specified by natural language instructions) e.g. "Pick up the Cup or else"
A rainbow brackets plugin for SublimeText4.
Deep reinforcement learning baselines base on OpenAI. More algorithms are included, such as Rainbow: Combining Improvements in Deep Reinforcement Learning
A collection of Deep Reinforcement Learning algorithms implemented with PyTorch to solve Atari games and classic control tasks like CartPole, LunarLander, and MountainCar.
PyTorch agents and tools for (Deep) Reinforcement Learning
PyTorch & Rainbow for Obstacle Tower Challenge
🚀⚡Fastest RainBow NitroGenerator! 🚀⚡
OpenAI Gym compatible reinforcement learning environment for Space Fortress https://arxiv.org/abs/1809.02206
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