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.

• Type: Conference
• Year: 2001
• Where: ICANN
• Authors: Ivo Kwee, Marcus Hutter, Jürgen Schmidhuber
• Keywords: ICANN 2001, Market-based Rl, Neural Networks, Observable Markov Decision, Principle Applicable