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ECAI
2010
Springer

The Dynamics of Multi-Agent Reinforcement Learning

8 years 11 months ago
The Dynamics of Multi-Agent Reinforcement Learning
Abstract. Infinite-horizon multi-agent control processes with nondeterminism and partial state knowledge have particularly interesting properties with respect to adaptive control, such as the non-existence of Nash Equilibria (NE) or non-strict NE which are nonetheless points of convergence. The identification of reinforcement learning (RL) algorithms that are robust, accurate and efficient when applied to these general multi-agent domains is an open, challenging problem. This paper uses learning pressure fields as a means for evaluating RL algorithms in the context of multi-agent processes. Specifically, we show how to model partially observable infinite-horizon stochastic processes (single-agent) and games (multi-agent) within the Finite Analytic Stochastic Process framework. Taking long term average expected returns as utility measures, we show the existence of learning pressure fields: vector fields
Luke Dickens, Krysia Broda, Alessandra Russo
Added 08 Nov 2010
Updated 08 Nov 2010
Type Conference
Year 2010
Where ECAI
Authors Luke Dickens, Krysia Broda, Alessandra Russo
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