Adaptive Real-Time Dynamic Programming
Adaptive Real-Time Dynamic Programming (ARTDP) is an algorithm that allows an agent to improve its behavior while interacting over time with an incompletely known dynamic environment. It can also be viewed as a heuristic search algorithm for finding shortest paths in incompletely known stochastic domains. ARTDP is based on Dynamic Programming (DP), but unlike conventional DP, which consists ofoff-line algorithms, ARTDP is an on-line algorithm because it uses agent behavior to guide its computation. ARTDP is adaptive because it does not need a complete and accurate model of the environment but learns a model from data collected during agent-environment interaction. When a good model is available, Real-Time Dynamic Programming (RTDP) is applicable, which is ARTDP without the model-learning component.
Motivation and Background
RTDP combines strengths of heuristic search and DP. Like heuristic search – and unlike conventional DP – it does not have to evaluate...
- Bonet B, Geffner H (2003a) Labeled RTDP: improving the convergence of real-time dynamic programming. In: Proceedings of the 13th international conference on automated planning and scheduling (ICAPS-2003), TrentoGoogle Scholar
- Bonet B, Geffner H (2003b) Faster heuristic search algorithms for planning with uncertainty and full feedback. In: Proceedings of the international joint conference on artificial intelligence (IJCAI-2003), AcapulcoGoogle Scholar
- Feng Z, Hansen E, Zilberstein S (2003) Symbolic generalization for on-line planning. In: Proceedings of the 19th conference on uncertainty in artificial intelligence, AcapulcoGoogle Scholar
- Jalali A, Ferguson M (1989) Computationally efficient control algorithms for Markov chains. In: Proceedings of the 28th conference on decision and control, Tampa, pp 1283–1288Google Scholar
- Smith T, Simmons R (2006) Focused real-time dynamic programming for MDPs: squeezing more out of a heuristic. In: Proceedings of the national conference on artificial intelligence (AAAI). AAAI Press, BostonGoogle Scholar
- Sutton R (1990) Integrated architectures for learning, planning, and reacting based on approximating dynamic programming. In: Proceedings of the 7th international conference on machine learning. Morgan Kaufmann, San Mateo, pp 216–224Google Scholar