The Drone Swarm Search project provides an environment for SAR missions built on PettingZoo, where agents, represented by drones, are tasked with locating targets identified as shipwrecked individu…

A benchmark comparing MPC and PPO for safe autonomous lane-keeping using a kinematic bicycle model. Includes a Gym-style environment, shooting-based MPC, and a PyTorch PPO agent. Not affiliated wit…

MAST: Multi-Agent Safe Transformer for Reinforcement Learning

Thrid-party safety-gym safe exploration environments for autonomous driving. Also environment used for IROS 2021 paper: Model-based Constrained Reinforcement Learning using Generalized Control Barr…

Code for GPMF(Partially Observable Mean Field Multi-Agent Reinforcement Learning Based on Graph Attention Network for UAV Swarms) accepted in Drones

Fuzzy Logic and Fuzzy Inference for Python 3

JMLR: OmniSafe is an infrastructural framework for accelerating SafeRL research.

Open Source OpenAI gym compatible TORCS Driving Environment

Robust and safe deep reinforcement learning algorithms

This project uses the pytorch package to implement DQN and DDPG models to automate the LunarLander-v2 and LunarLanderContinuous-v2 games.

Deep RL implementations. DQN, SAC, DDPG, TD3, PPO and VPG implemented in pytorch. Tested Env: LunarLander-v2 and Pendulum-v0.

PyTorch application of reinforcement learning algorithm in OpenAI LunarLander - DDPG

This repo contains PPO implementation in PyTorch for LunarLander-v2

Implementation of reinforcement learning algorithms for the OpenAI Gym environment LunarLander-v2

OpenAI LunarLander-v2 DeepRL-based solutions (DQN, DuelingDQN, D3QN)

AAAI 2024 Papers: Explore a comprehensive collection of innovative research papers presented at one of the premier artificial intelligence conferences. Seamlessly integrate code implementations for…

NeurIPS 2023: Safety-Gymnasium: A Unified Safe Reinforcement Learning Benchmark

Implementation of the VIPER algorithm introduced in "Verifiable Reinforcement Learning via Policy Extraction" by Bastani et al.

Alpha-divergences Minimization for Deep Gaussian Process Models

PyTorch Implementation of MADDPG (Lowe et. al. 2017)

[ICLR-2025] POGEMA stands for Partially-Observable Grid Environment for Multiple Agents. This is a grid-based environment that was specifically designed to be flexible, tunable and scalable. It can…

Deep Reinforcement Learning Lab, a platform designed to make DRL technology and fun for everyone