Evaluating Hierarchies through A Partially Observable Markov Decision Processes Methodology.
Weipéng HuángGuangyuan PiaoRaúl MorenoNeil J. HurleyPublished in: CoRR (2019)
Keyphrases
- partially observable markov decision processes
- planning under uncertainty
- finite state
- belief state
- reinforcement learning
- belief space
- optimal policy
- dynamical systems
- partially observable domains
- stochastic domains
- markov decision processes
- decision problems
- partially observable stochastic games
- continuous state
- dynamic programming
- partially observable
- planning problems
- state space
- search algorithm
- multi agent
- model checking
- heuristic search
- partial observability
- sequential decision making problems
- machine learning