Proceedings of the seventh international conference (1990) on Machine learning
Made-up minds: a constructivist approach to artificial intelligence
Made-up minds: a constructivist approach to artificial intelligence
A survey of algorithmic methods for partially observed Markov decision processes
Annals of Operations Research
Learning to Perceive and Act by Trial and Error
Machine Learning
Efficient Exploration In Reinforcement Learning
Efficient Exploration In Reinforcement Learning
Learning in embedded systems
Input generalization in delayed reinforcement learning: an algorithm and performance comparisons
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
Planning and acting in partially observable stochastic domains
Artificial Intelligence
RL-Based Memory Controller for Scalable Autonomous Systems
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
A Modified Memory-Based Reinforcement Learning Method for Solving POMDP Problems
Neural Processing Letters
Incremental pruning: a simple, fast, exact method for partially observable Markov decision processes
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Feature extraction for decision-theoretic planning in partially observable environments
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Model-based online learning of POMDPs
ECML'05 Proceedings of the 16th European conference on Machine Learning
Feature reinforcement learning in practice
EWRL'11 Proceedings of the 9th European conference on Recent Advances in Reinforcement Learning
EWRL'11 Proceedings of the 9th European conference on Recent Advances in Reinforcement Learning
EWRL'11 Proceedings of the 9th European conference on Recent Advances in Reinforcement Learning
On the Computational Complexity of Stochastic Controller Optimization in POMDPs
ACM Transactions on Computation Theory (TOCT)
Recognizing internal states of other agents to anticipate and coordinate interactions
EUMAS'11 Proceedings of the 9th European conference on Multi-Agent Systems
The duality of state and observation in probabilistic transition systems
TbiLLC'11 Proceedings of the 9th international conference on Logic, Language, and Computation
Abstraction in Model Based Partially Observable Reinforcement Learning Using Extended Sequence Trees
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
| Hi-index | 0.00 |
It is known that Perceptual Aliasing may significantly diminish the effectiveness of reinforcement learning algorithms [Whitehead and Ballard, 1991]. Perceptual aliasing occurs when multiple situations that are indistinguishable from immediate perceptual input require different responses from the system. For example, if a robot can only see forward, yet the presence of a battery charger behind it determines whether or not it should backup, immediate perception alone is insufficient for determining the most appropriate action. It is problematic since reinforcement algorithms typically learn a control policy from immediate perceptual input to the optimal choice of action. This paper introduces the predictive distinctions approach to compensate for perceptual aliasing caused from incomplete perception of the world. An additional component, a predictive model, is utilized to track aspects of the world that may not be visible at all times. In addition to the control policy, the model must also be learned, and to allow for stochastic actions and noisy perception, a probabilistic model is learned from experience. In the process, the system must discover, on its own, the important distinctions in the world. Experimental results are given for a simple simulated domain, and additional issues are discussed.