**arXiv ID:** 0105025v1 **Authors:** Ivo Kwee, Marcus Hutter, Juergen Schmidhuber **Published:** 2001-05-15T19:07:28Z **Abstract:** 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...
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# Market-Based Reinforcement Learning in Partially Observable Worlds
**arXiv ID:** 0105025v1
**Authors:** Ivo Kwee, Marcus Hutter, Juergen Schmidhuber
**Published:** 2001-05-15T19:07:28Z
**Abstract:**
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.
## Skill Description
This skill is generated from the arXiv paper: Market-Based Reinforcement Learning in Partially Observable Worlds (0105025v1).
## How to Use
[To be filled in by the user or by future automation]
## References
- [arXiv:0105025v1](http://arxiv.org/abs/cs/0105025v1)
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