Market-Based Reinforcement Learning in Partially Observable Worlds
<|reference_start|>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.<|reference_end|>
@article{kwee2001market-based,
    title={Market-Based Reinforcement Learning in Partially Observable Worlds},
    author={Ivo Kwee, Marcus Hutter, Juergen Schmidhuber},
    journal={Lecture Notes in Computer Science (LNCS 2130), Proceeding of the
  International Conference on Artificial Neural Networks ICANN (2001) 865-873},
    year={2001},
    number={IDSIA-10-01},
    archivePrefix={arXiv},
    eprint={cs/0105025},
    primaryClass={cs.AI cs.LG cs.MA cs.NE}
}