# Associative reinforcement learning: A generate and test algorithm

@article{Kaelbling2004AssociativeRL, title={Associative reinforcement learning: A generate and test algorithm}, author={Leslie Pack Kaelbling}, journal={Machine Learning}, year={2004}, volume={15}, pages={299-319} }

An agent that must learn to act in the world by trial and error faces thereinforcement learning problem, which is quite different from standard concept learning. Although good algorithms exist for this problem in the general case, they are often quite inefficient and do not exhibit generalization. One strategy is to find restricted classes of action policies that can be learned more efficiently. This paper pursues that strategy by developing an algorithm that performans an on-line search…

## 23 Citations

### Associative reinforcement learning: Functions ink-DNF

- Computer ScienceMachine Learning
- 2004

Algorithms are developed that can efficiently learn action maps that are expressible ink-DNF and are shown to have very good performance.

### Associative Reinforcement Learning: Functions in k-DNF

- Computer ScienceMachine Learning
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Algorithms that can efficiently learn action maps that are expressible in k-DNF are developed and are shown to have very good performance.

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For two cases, one in which a continuous-valued reward is given by applying the unknown linear function, and another in which the probability of receiving the larger of binary-valued rewards is obtained, lower bounds are provided that show that the rate of convergence is nearly optimal.

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This thesis investigates an online and incremental representation search algorithm called Generate and Test, which continually replaces the least useful features with newly generated features, and empirically shows that this new tester can improve representations better than the magnitude-based tester.

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This thesis argues that both the multi-armed bandit problem and the best arm identification problem can be tackled effectively using Thompson Sampling based approaches and provides empirical evidence to support this claim.

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A version of a theorem that originated from population genetics and has been later adopted in evolutionary computation theory that will lead to novel Monte‐Carlo sampling algorithms that provably increase the AI potential is established.

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- Computer ScienceJ. Mach. Learn. Res.
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It is shown how a standard tool from statistics, namely confidence bounds, can be used to elegantly deal with situations which exhibit an exploitation-exploration trade-off, and improves the regret from O(T3/4) to T1/2.

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This work illustrates the emergence of different types of information-gain, termed active inference and active learning, and shows how these forms of exploration induce distinct patterns of ‘Bayes-optimal’ behaviour.

### Conceptual Commitments of the LIDA Model of Cognition

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The intention is to initiate a discussion among AGI researchers about which conceptual commitments are essential, or particularly useful, toward creating AGI agents, and to describe the hypotheses underlying one such model, the Learning Intelligent Distribution Agent (LIDA) Model.

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