Researchers have introduced "excursion policies" as a novel approach to off-policy reinforcement learning. This method involves an agent following a target policy for a set number of steps before returning to a behavior policy. The study provides theoretical underpinnings for these policies through dynamic programming results and develops corresponding temporal-difference algorithms. Excursion policies demonstrate a trade-off between efficiency and stability, performing comparably to mixture policies on continuous control benchmarks. AI
IMPACT Introduces a new theoretical framework and algorithms for improving the efficiency and stability of off-policy reinforcement learning.
RANK_REASON Academic paper detailing a new method in reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]
- arXiv
- Excursion Policies
- Hugging Face
- Off-Policy Reinforcement Learning: Optimal Operational Control for Two-Time-Scale Industrial Processes
- reinforcement learning
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