“Market-Based Reinforcement Learning in Partially Observable Worlds”, Ivo Kwee, Marcus Hutter, Juergen Schmidhuber2001-05-15 (MARL)⁠:

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.