Market-based reinforcement learning in partially observable worlds
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.
|Collections||ANU Research Publications|
|Source:||Artificial Neural Networks - ICANN 2001 Proceedings|
|Kwee et al Market based Reinforcement Learning 2001.pdf||134 kB||Adobe PDF|
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