[Paper Review] Inverse Reinforcement Learning in Swarm Systems

Adrian Šošić, Wasiur R. KhudaBukhsh+2 more|arXiv (Cornell University)|May 8, 2017

Reinforcement Learning in Robotics34 references29 citations

TL;DR

This paper introduces swarMDP, a novel framework for inverse reinforcement learning in homogeneous large-scale swarm systems, reducing multi-agent IRL to a single-agent problem by exploiting symmetry in agent value functions. It proposes a heterogeneous learning scheme that recovers local reward models enabling accurate replication of observed global swarm dynamics in two test systems.

ABSTRACT

Inverse reinforcement learning (IRL) has become a useful tool for learning behavioral models from demonstration data. However, IRL remains mostly unexplored for multi-agent systems. In this paper, we show how the principle of IRL can be extended to homogeneous large-scale problems, inspired by the collective swarming behavior of natural systems. In particular, we make the following contributions to the field: 1) We introduce the swarMDP framework, a sub-class of decentralized partially observable Markov decision processes endowed with a swarm characterization. 2) Exploiting the inherent homogeneity of this framework, we reduce the resulting multi-agent IRL problem to a single-agent one by proving that the agent-specific value functions in this model coincide. 3) To solve the corresponding control problem, we propose a novel heterogeneous learning scheme that is particularly tailored to the swarm setting. Results on two example systems demonstrate that our framework is able to produce meaningful local reward models from which we can replicate the observed global system dynamics.

Motivation & Objective

• To address the lack of inverse reinforcement learning (IRL) applications in multi-agent and swarm systems.
• To formalize a sub-class of decentralized partially observable Markov decision processes (MDPs) with swarm-specific characteristics.
• To exploit agent homogeneity in large-scale swarms to reduce the multi-agent IRL problem to a single-agent problem.
• To design a learning scheme tailored for swarm dynamics that enables recovery of local reward functions from global demonstrations.
• To validate the framework’s ability to replicate observed global system behavior using learned local rewards.

Proposed method

• Proposes swarMDP, a sub-class of decentralized POMDPs with a formalized swarm characterization for homogeneous multi-agent systems.
• Proves that under homogeneity, agent-specific value functions in swarMDP are identical, enabling reduction of multi-agent IRL to single-agent IRL.
• Develops a heterogeneous learning scheme specifically designed for the swarm setting, allowing efficient policy learning from demonstration data.
• Uses demonstration data to infer local reward functions that, when optimized, reproduce the observed global system dynamics.
• Applies the framework to two example systems to validate the recovery of meaningful local rewards and accurate global behavior replication.

Experimental results

Research questions

• RQ1 Can inverse reinforcement learning be effectively extended to large-scale, homogeneous multi-agent systems with collective behavior?
• RQ2 To what extent can the symmetry and homogeneity of swarm systems be leveraged to reduce multi-agent IRL to a single-agent problem?
• RQ3 Can a tailored learning scheme recover local reward functions that accurately reproduce observed global swarm dynamics?
• RQ4 How well can the proposed framework replicate complex global behaviors from local reward models in real-world-inspired swarm systems?

Key findings

• The framework successfully reduces the multi-agent IRL problem to a single-agent problem by proving that agent-specific value functions are identical under homogeneity.
• The proposed heterogeneous learning scheme enables effective policy learning in swarm systems by focusing on local reward inference from global demonstrations.
• In both example systems, the learned local reward models were able to replicate the observed global dynamics with high fidelity.
• The results demonstrate that meaningful local reward functions can be recovered from demonstration data, even in large-scale, decentralized settings.