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ICANN
2001
Springer

Market-Based Reinforcement Learning in Partially Observable Worlds

9 years 3 months ago
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.
Ivo Kwee, Marcus Hutter, Jürgen Schmidhuber
Added 29 Jul 2010
Updated 29 Jul 2010
Type Conference
Year 2001
Where ICANN
Authors Ivo Kwee, Marcus Hutter, Jürgen Schmidhuber
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