Reinforcement Learning under External Influence: Guarantees, Algorithms, and Sample Complexity
Quick summary
arXiv:2305.16056v5 Announce Type: replace-cross Abstract: In this paper, we study the problem of reinforcement learning under the influence of external events. For this, we consider Markov decision processes with continuous state and action spaces whose transition dynamics are perturbed by an external process in a non-Markovian manner. First, we establish the conditions under which the problem becomes tractable, allowing it to be addressed by considering only a finite history of events, based on the properties of the perturbations introduced by the exogenous process. We propose and theoretical
Key takeaways
- arXiv:2305.16056v5 Announce Type: replace-cross Abstract: In this paper, we study the problem of reinforcement learning under the influence of external events.
- For this, we consider Markov decision processes with continuous state and action spaces whose transition dynamics are perturbed by an external process in a non-Markovian manner.
- First, we establish the conditions under which the problem becomes tractable, allowing it to be addressed by considering only a finite history of events, based on the properties of the perturbations introduced by the exogenous process.
Why it matters
The value of this work lies as much in how it was tested as in the claim itself. Sample design, baselines, uncertainty and replication help separate a laboratory result from real-world impact.
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