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
Stochastic dynamic programming with factored representations
Artificial Intelligence
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Probabilistic policy reuse in a reinforcement learning agent
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Probabilistic Policy Reuse for inter-task transfer learning
Robotics and Autonomous Systems
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We are interested in the following general question: is it possible to abstract knowledge that is generated while learning the solution of a problem, so that this abstraction can accelerate the learning process? Moreover, is it possible to transfer and reuse the acquired abstract knowledge to accelerate the learning process for future similar tasks? We propose a framework for conducting simultaneously two levels of reinforcement learning, where an abstract policy is learned while learning of a concrete policy for the problem, such that both policies are refined through exploration and interaction of the agent with the environment. We explore abstraction both to accelerate the learning process for an optimal concrete policy for the current problem, and to allow the application of the generated abstract policy in learning solutions for new problems. We report experiments in a robot navigation environment that show our framework to be effective in speeding up policy construction for practical problems and in generating abstractions that can be used to accelerate learning in new similar problems.