On-Policy and Off-Policy Learning for Large Action Spaces

This thesis studies policy learning in interactive systems where an agent observes a context, selects an action from a very large set, and receives partial feedback. The main framework is contextual bandits, with two paradigms: on-policy learning, where the agent interacts sequentially with the environment and minimizes regret, and off-policy learning, where it learns from logged data collected by a logging policy. In large action spaces, both settings face major challenges: inefficient explorat

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ethics.ai (30 July 2026), “On-Policy and Off-Policy Learning for Large Action Spaces,” evidence record 15223, https://ethics.ai/record/15223 (originally published by arXiv cs.AI).

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