2007
NeurIPS
NeurIPS 2007
Stable Dual Dynamic Programming
Abstract
Recently, we have introduced a novel approach to dynamic programming and re- inforcement learning that is based on maintaining explicit representations of sta- tionary distributions instead of value functions. In this paper, we investigate the convergence properties of these dual algorithms both theoretically and empirically, and show how they can be scaled up by incorporating function approximation.
🌉
Interdisciplinary Bridge
- Artificial Intelligence and Reinforcement Learning
📈
Trend Setter
- Markov Decision Process
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Keyword Pioneer
- stationary distribution
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Hot Topic Early Bird
- value function
Authors
Topics
Artificial Intelligence > Core AI > Agent Systems
Machine Learning > Optimization & Theory > Optimization
Reinforcement Learning > Methods > Deep RL
Reinforcement Learning > Applications > Value Iteration
Machine Learning > Learning Types > Reinforcement Learning
Machine Learning > Learning Types > Markov Decision Process