Reinforcement learning with hierarchies of machines
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Knowlege in action: logical foundations for specifying and implementing dynamical systems
Knowlege in action: logical foundations for specifying and implementing dynamical systems
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
The MAXQ Method for Hierarchical Reinforcement Learning
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Decision-Theoretic, High-Level Agent Programming in the Situation Calculus
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
State abstraction for programmable reinforcement learning agents
Eighteenth national conference on Artificial intelligence
Exploiting first-order regression in inductive policy selection
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Game theoretic Golog under partial observability
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Symbolic dynamic programming for first-order MDPs
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Combining probabilities, failures and safety in robot control
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Concurrent hierarchical reinforcement learning
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Integrating relational reinforcement learning with reasoning about actions and change
ILP'11 Proceedings of the 21st international conference on Inductive Logic Programming
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We present a novel approach to adaptive multi-agent programming, which is based on an integration of the agent programming language GTGolog with adaptive dynamic programming techniques. GTGolog combines explicit agent programming in Golog with multi-agent planning in stochastic games. A drawback of this framework, however, is that the transition probabilities and reward values of the domain must be known in advance and then cannot change anymore. But such data is often not available in advance and may also change over the time. The adaptive generalization of GTGolog in this paper is directed towards letting the agents themselves explore and adapt these data, which is more useful for realistic applications. We use high-level programs for generating both abstract states and optimal policies, which benefits from the deep integration between action theory and high-level programs in the Golog framework.