On the Complexity of Reachability in Parametric Markov Decision Processes.
Tobias WinklerSebastian JungesGuillermo A. PérezJoost-Pieter KatoenPublished in: CoRR (2019)
Keyphrases
- markov decision processes
- state space
- optimal policy
- finite state
- transition matrices
- dynamic programming
- reachability analysis
- policy iteration
- reinforcement learning
- planning under uncertainty
- decision problems
- average reward
- reinforcement learning algorithms
- partially observable
- infinite horizon
- finite horizon
- decision theoretic planning
- factored mdps
- computational complexity
- state and action spaces
- action sets
- model based reinforcement learning
- decision processes
- probabilistic planning
- decision diagrams
- machine learning