FinRL®: 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 Pokémon 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 reinforement learning. Chaser and runner.

Multi-agent collaboration using Reinforcement Learning

Multiplayer Game, Security, DF Service Implementation, Genetic Algorithm Implementation, Multi-Agent Systems Enhanced With Q-Learning Implementation For Improved Decision-Making.

Regime-Invariant Specialist Pools (RISP): reward-independent specialist retention + episode-invariant decision-focused training for non-stationary markets. Theory, 11-arm experiments, three papers — honest nulls included. Companion to GAUSE.

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Practical multi-agent communication sandbox for topology-aware compression and result visualization.