Researchers have established the convergence of a modified Gradient Temporal-Difference (TD) learning algorithm, known as constrained TDRC($\lambda$), under non-i.i.d. off-policy data. This work also introduces TDRC($\lambda$)-Huber, an extension designed to mitigate sensitivity to large Bellman errors while maintaining the TDRC($\lambda$) fixed point. The convergence proofs utilize an ODE-based weak-convergence method from stochastic approximation theory, providing computable sufficient conditions and identifying a shared regularization-based convergence mechanism. AI
IMPACT Establishes theoretical convergence guarantees for advanced reinforcement learning algorithms, potentially improving their stability and performance in complex environments.
RANK_REASON Academic paper detailing theoretical advancements in machine learning algorithms. [lever_c_demoted from research: ic=1 ai=1.0]
- alphaXiv
- arXiv
- CatalyzeX
- DagsHub
- Huber Bellman-error
- Hugging Face
- IArxiv
- TDRC($\lambda$)
- TDRC($\lambda$)-Huber
AI-generated summary · Google Gemini · from 1 sources. How we write summaries →
