A pulse neural network reinforcement learning algorithm for partially observable Markov decision processes.
Koichiro TakitaMasafumi HagiwaraPublished in: Systems and Computers in Japan (2005)
Keyphrases
- partially observable markov decision processes
- neural network
- finite state
- planning under uncertainty
- belief state
- reinforcement learning
- dynamical systems
- dynamic programming
- decision problems
- optimal policy
- belief space
- markov decision processes
- state space
- continuous state
- partially observable domains
- multi agent
- partial observability
- markov chain
- partially observable stochastic games
- stochastic domains
- sequential decision making problems
- partially observable markov
- partially observable
- point based value iteration
- dec pomdps
- partially observable markov decision process
- function approximators
- planning problems
- graphical models
- data mining