Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Dynamic Programming and Optimal Control, Vol. II
Dynamic Programming and Optimal Control, Vol. II
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
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New proofs for two extensions to value iteration are derived when the type of initialisation of the value function is considered. Theoretical requirements that guarantee the convergence of backward value iteration and weaker requirements for the convergence of backups based on best actions only are identified. Experimental results show that standard value iteration performs significantly faster with simple extensions that are investigated in this work.