There are 0 repository under torchrl topic.
A PyTorch library for all things Reinforcement Learning (RL) for Combinatorial Optimization (CO)
BricksRL: A Platform for Democratizing Robotics and Reinforcement Learning Research and Education with LEGO
RouteRL is a multi-agent reinforcement learning framework for modeling and simulating the collective route choices of humans and autonomous vehicles.
This project is a pipeline that connects a Matlab simulation (Simulink) to an OpenAI Gym wrapper for PyTorch Reinforcement Learning using the DQN algorithm.
Multi-Agent Reinforcement Learning for Level-Based Foraging Using TorchRL.
An adaption of the Flatland environment for TorchRL.
MARL research project, where agents collaboratively self-organize inside dynamically generated geometric patterns.
MARL research project in which rescuer and rescuee agents collaborate to navigate and succeed in complex, obstacle-rich environments.
A highly modular and extensible PyTorch-based reinforcement learning library.
Small prototype to show RLBench usage with TorchRL
Small prototype to show RoboHive usage with TorchRL for visual deep reinforcement learning
🤖 PPO and DQN reinforcement learning algorithms implemented with PyTorch and TorchRL for autonomous driving.
MARL research project, based on the famous board game "Scotland Yard".
MARL research project, where agents collaboratively optimize a given objective function in a multi-dimensional search space.
MARL research project, where agents must collaboratively solve multi-agent pathfinding tasks in complex, obstacle-rich environments.
A Multi-Agent Reinforcement Learning (MARL) research project where patrolling and intruder agents engage in an adversarial setting, continuously adapting and countering each other’s strategies.
MARL research project, where multiple agents (ants) interact in a shared 2D environment containing scattered items of different categories.
Application of deep reinforcement learning (DQN and PPO) for automated trading on HPC system, comparing performance across CPU/GPU nodes
Using Reinforcement Learning to play Dark Souls III
This project aims to improve the efficiency of online reinforcement learning by incorporating bisimulation-based metrics into the experience replay process. Bisimulation Prioritized Experience Replay (BPER) prioritizes experiences based on their behavioral differences.
Inspired by the series "Squid Game", this project requires agents to learn coordinated decision-making and spatial negotiation in a competitive-cooperative setting.
Clone of the Mars: Mars game using Pygame as a Reinforcement Learning Gym 🚀
Envpool wrapper for Envs written in Python. This module defines a protocol, allowing to wrap aribitrary environments written in Python to be executed using envpool.
Training a PPO agent to play chess with pretraining and self-learning using PyTorch Lightning and TorchRL