FinRL(r): Financial Reinforcement Learning.
Paper list of multi-agent reinforcement learning (MARL)
VMAS is a vectorized differentiable simulator designed for efficient Multi-Agent Reinforcement Learning benchmarking. It is comprised of a vectorized 2D physics engine written in PyTorch and a set of challenging multi-robot scenarios. Additional scenarios can be implemented through a simple and modular interface.
Multi-Agent Reinforcement Learning (MARL) papers
A Collection of Multi-Agent Reinforcement Learning (MARL) Resources
A selection of state-of-the-art research materials on decision making and motion planning.
An SDK for multi-agent collaborative perception.
[NeurIPS2021] Learning Distilled Collaboration Graph for Multi-Agent Perception
Active visual tracking library based on PyTorch.
A curated list of multiagent learning and related area resources.
A curated list of awesome multi-agent learning papers
An AI benchmark for Pokemon VGC with agent implementations using multi-agent reinforcement learning, behavior cloning, LLMs, and heuristics
We reproduced DeepMind's results and implement a meta-learning (MLSH) agent which can generalize across minigames.
Computing mixed-strategy Nash Equilibria for games involving multiple players
Multi-agent collaboration using Reinforcement Learning
Multi agent reinforement learning. Chaser and runner.
Multiplayer Game, Security, DF Service Implementation, Genetic Algorithm Implementation, Multi-Agent Systems Enhanced With Q-Learning Implementation For Improved Decision-Making.
This repository contains an implementation of a deep learning architecture designed for unsupervised or self-supervised classification tasks. The architecture consists of two components: a classifier and an aligner.
Decentralized AI Memory Network (DAMN) -- a persistent learning and memory-sharing infrastructure for autonomous agents. Blockchain + IPFS-based memory layer enabling cross-agent experience reuse and scalable distributed intelligence.
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