Informative Policy Representations in Multi-Agent Reinforcement Learning via Joint-Action Distributions

06/10/2021
by   Yifan Yu, et al.
0

In multi-agent reinforcement learning, the inherent non-stationarity of the environment caused by other agents' actions posed significant difficulties for an agent to learn a good policy independently. One way to deal with non-stationarity is agent modeling, by which the agent takes into consideration the influence of other agents' policies. Most existing work relies on predicting other agents' actions or goals, or discriminating between their policies. However, such modeling fails to capture the similarities and differences between policies simultaneously and thus cannot provide useful information when generalizing to unseen policies. To address this, we propose a general method to learn representations of other agents' policies via the joint-action distributions sampled in interactions. The similarities and differences between policies are naturally captured by the policy distance inferred from the joint-action distributions and deliberately reflected in the learned representations. Agents conditioned on the policy representations can well generalize to unseen agents. We empirically demonstrate that our method outperforms existing work in multi-agent tasks when facing unseen agents.

READ FULL TEXT

Authors

page 6

11/28/2019

Multi-Agent Deep Reinforcement Learning with Adaptive Policies

We propose a novel approach to address one aspect of the non-stationarit...
08/04/2021

Model-Based Opponent Modeling

When one agent interacts with a multi-agent environment, it is challengi...
08/30/2021

Learning Meta Representations for Agents in Multi-Agent Reinforcement Learning

In multi-agent reinforcement learning, the behaviors that agents learn i...
03/19/2020

Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

In many real-world settings, a team of agents must coordinate its behavi...
11/12/2020

Learning Latent Representations to Influence Multi-Agent Interaction

Seamlessly interacting with humans or robots is hard because these agent...
08/04/2021

Offline Decentralized Multi-Agent Reinforcement Learning

In many real-world multi-agent cooperative tasks, due to high cost and r...
09/17/2021

APIA: An Architecture for Policy-Aware Intentional Agents

This paper introduces the APIA architecture for policy-aware intentional...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.