Genetic algorithms in noisy environments
Machine Language
Memoryless policies: theoretical limitations and practical results
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
Noise, sampling, and efficient genetic algorthms
Noise, sampling, and efficient genetic algorthms
Cultural algorithms: theory and applications
New ideas in optimization
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
An Analysis of Direct Reinforcement Learning in Non-Markovian Domains
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
A Cultural Algorithm Framework to Evolve Multi-Agent Cooperation with Evolutionary Programming
EP '97 Proceedings of the 6th International Conference on Evolutionary Programming VI
Adaptive Inventory Control for Nonstationary Demand and Partial Information
Management Science
Culturizing Differential Evolution for Constrained Optimization
ENC '04 Proceedings of the Fifth Mexican International Conference in Computer Science
Integration of genetic algorithm and cultural algorithms for constrained optimization
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
On the complexity of solving Markov decision problems
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
IEEE Transactions on Evolutionary Computation
Local performance of the (1 + 1)-ES in a noisy environment
IEEE Transactions on Evolutionary Computation
Neuroevolutionary Inventory Control in Multi-Echelon Systems
ADT '09 Proceedings of the 1st International Conference on Algorithmic Decision Theory
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Reinforcement Learning algorithms such as SARSA with an eligibility trace, and Evolutionary Computation methods such as genetic algorithms, are competing approaches to solving Partially Observable Markov Decision Processes (POMDPs) which occur in many fields of Artificial Intelligence. A powerful form of evolutionary algorithm that has not previously been applied to POMDPs is the cultural algorithm, in which evolving agents share knowledge in a belief space that is used to guide their evolution. We describe a cultural algorithm for POMDPs that hybridises SARSA with a noisy genetic algorithm, and inherits the latter's convergence properties. Its belief space is a common set of state-action values that are updated during genetic exploration, and conversely used to modify chromosomes. We use it to solve problems from stochastic inventory control by finding memoryless policies for nondeterministic POMDPs. Neither SARSA nor the genetic algorithm dominates the other on these problems, but the cultural algorithm outperforms the genetic algorithm, and on highly non-Markovian instances also outperforms SARSA.