arXiv
Open Access
2016
On Avoidance Learning with Partial Observability
Tom J. Ameloot
Abstrak
We study a framework where agents have to avoid aversive signals. The agents are given only partial information, in the form of features that are projections of task states. Additionally, the agents have to cope with non-determinism, defined as unpredictability on the way that actions are executed. The goal of each agent is to define its behavior based on feature-action pairs that reliably avoid aversive signals. We study a learning algorithm, called A-learning, that exhibits fixpoint convergence, where the belief of the allowed feature-action pairs eventually becomes fixed. A-learning is parameter-free and easy to implement.
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Tom J. Ameloot
Akses Cepat
Informasi Jurnal
- Tahun Terbit
- 2016
- Bahasa
- en
- Sumber Database
- arXiv
- Akses
- Open Access ✓