Market-Based Reinforcement Learning in Partially Observable Worlds

Details Market-Based Reinforcement Learning in Partially Observable Worlds Market-Based Reinforcement Learning in Partially Observable Worlds

2001-05-15

arxiv.org/abs/cs/0105025v1

Lecture Notes in Computer Science (LNCS 2130), Proceeding of the International Conference on Artificial Neural Networks ICANN

Computer Science

Artificial Intelligence

8 LaTeX pages, 2 postscript figures

Scientific paper

Unlike traditional reinforcement learning (RL), market-based RL is in principle applicable to worlds described by partially observable Markov Decision Processes (POMDPs), where an agent needs to learn short-term memories of relevant previous events in order to execute optimal actions. Most previous work, however, has focused on reactive settings (MDPs) instead of POMDPs. Here we reimplement a recent approach to market-based RL and for the first time evaluate it in a toy POMDP setting.

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