First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The paper presents an interesting idea of using a robust formulation to fit a value function given aggregate states. The robust formulation leads to a much stronger error bound than what is achieved by regular approximate value iteration. The key is in using the algorithm to effectively select weights applied to states within each aggregate. On the negative side, the authors do not do a good job at presenting their notation and algorithm in a clear way, and the computational results are difficult to understand.

Neural Information Processing Systems http://nips.cc/

We describe how to use robust Markov decision processes for value function approximation with state aggregation. The robustness serves to reduce the sensitivity to the approximation error of sub-optimal policies in comparison to classical methods such as fitted value iteration.

We describe how to use robust Markov decision processes for value function approximation with state aggregation. The robustness serves to reduce the sensitivity to the approximation error of sub-optimal policies in comparison to classical methods such as fitted value iteration.

We describe how to use robust Markov decision processes for value function approximation with state aggregation. The robustness serves to reduce the sensitivity to the approximation error of sub-optimal policies in comparison to classical methods such as fitted value iteration.

In this paper we propose an extension to the RAAM by Pollack. Data encoded in an LRAAM can be accessed by pointer as well as by content. Direct access by content can be achieved by transform(cid:173) ing the encoder network of the LRAAM into an analog Hopfield network with hidden units. Different access procedures can be defined depending on the access key. Sufficient conditions on the asymptotical stability of the associated Hopfield network are briefly introduced.

We describe how to use robust Markov decision processes for value function approximation with state aggregation. The robustness serves to reduce the sensitivity to the approximation error of sub-optimal policies in comparison to classical methods such as fitted value iteration. Our experimental results show that using the robust representation can significantly improve the solution quality with minimal additional computational cost. Papers published at the Neural Information Processing Systems Conference.

We describe how to use robust Markov decision processes for value function approximation with state aggregation. The robustness serves to reduce the sensitivity to the approximation error of sub-optimal policies in comparison to classical methods such as fitted value iteration. This results in reducing the bounds on the gamma-discounted infinite horizon performance loss by a factor of 1/(1-gamma) while preserving polynomial-time computational complexity. Our experimental results show that using the robust representation can significantly improve the solution quality with minimal additional computational cost.

I will describe my recent results on the automatic development of fixedwidth recursive distributed representations of variable-sized hierarchal data structures. One implication of this wolk is that certain types of AIstyle data-structures can now be represented in fixed-width analog vectors. Simple inferences can be perfonned using the type of pattern associations that neural networks excel at Another implication arises from noting that these representations become self-similar in the limit Once this door to chaos is opened.

I will describe my recent results on the automatic development of fixedwidth recursive distributed representations of variable-sized hierarchal data structures. One implication of this wolk is that certain types of AIstyle data-structures can now be represented in fixed-width analog vectors. Simple inferences can be perfonned using the type of pattern associations that neural networks excel at Another implication arises from noting that these representations become self-similar in the limit Once this door to chaos is opened.