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Planning using hierarchical constrained Markov decision processes.
Seyedshams Feyzabadi
Stefano Carpin
Published in:
Auton. Robots (2017)
Keyphrases
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optimal solution
markov decision processes
macro actions
decision theoretic planning
planning under uncertainty
partially observable
finite state
state space
linear programming
optimal policy
reinforcement learning
transition matrices
reachability analysis
objective function
partially observable markov decision processes
infinite horizon
policy iteration
factored mdps
markov decision problems
planning problems
dynamic programming
markov decision process
action space
reinforcement learning algorithms
ai planning
average reward
average cost
finite horizon
decision processes
domain independent
model based reinforcement learning
semi markov decision processes
heuristic search
action sets
state abstraction
probabilistic planning
finite number
data mining
discounted reward
state and action spaces
hierarchical reinforcement learning
planning domains
decision theoretic