In this paper we develop a Bayesian policy search approach for Multi-Agent RL (MARL), which is model-free and allows for priors on policy parameters. We present a novel optimization algorithm based on hybrid MCMC, which leverages both the prior and gradient information estimated from trajectories. Our experiments demonstrate the automatic discovery of roles through reinforcement learning in a r...

This article presents a theoretical framework for probably approximately correct (PAC) multi-agent reinforcement learning (MARL) algorithms Markov games. Using the idea of delayed Q-learning, this extends well-known Nash Q-learning algorithm to build new PAC MARL general-sum In addition guiding design provably algorithm, enables checking whether an arbitrary is PAC. Comparative numerical result...

In offline multi-agent reinforcement learning (MARL), agents estimate policies from a given dataset. We study reward-poisoning attacks in this setting where an exogenous attacker modifies the rewards dataset before see The wants to guide each agent into nefarious target policy while minimizing Lp norm of reward modification. Unlike on single-agent RL, we show that can install as Markov Perfect ...

We present PantheonRL, a multiagent reinforcement learning software package for dynamic training interactions such as round-robin, adaptive, and ad-hoc training. Our is designed around flexible agent objects that can be easily configured to support different interactions, handles fully general environments with mixed rewards n agents. Built on top of StableBaselines3, our works directly existin...

Calcium carbonate and calcium-magnesium carbonate in the form of limestone, dolomite, marl, chalk, and Oyster shell are one of the most widely utilized non-metallic materials in the industrial world. The largest use of limestone or calcium carbonate is in the cement industry where it is used as a source of CaO and also in the concrete industry where it is used as the primary coarse aggregate. F...

Abstract Offline reinforcement learning leverages previously collected offline datasets to learn optimal policies with no necessity access the real environment. Such a paradigm is also desirable for multi-agent (MARL) tasks, given combinatorially increased interactions among agents and However, in MARL, of pre-training online fine-tuning has not been studied, nor even or benchmarks MARL researc...