Above are papers related to the following paper: [Paper_ID] Paper #481073. 	[Title] PAC Adaptive Control of Linear Systems [Abstract] We consider a special case of reinforcement learning where the environment can be described by a linear system. The states of the environment and the actions the agent can perform are represented by real vectors and the system dynamic is given by a linear equation with a stochastic component. The problem is equivalent to the so-called linear quadratic regulator problem studied in the optimal and adaptive control literature. We propose a learning algorithm for that problem and analyze it in a PAC learning framework. Unlike the algorithms in the adaptive control literature, our algorithm actively explores the environment to learn an accurate model of the system faster. We show that the control law produced by our algorithm has, with high probability, a value that is close to that of an optimal policy relative to the magnitude of the initial state of the system. The time taken by the algorithm is polynomial in the dimension n of the state-space and in the dimension r of the action-space when the ratio n=r is a constant. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #134060. 	[Title] Expected Mistake Bound Model for On-Line Reinforcement Learning [Abstract] We propose a model of efficient on-line reinforcement learning based on the expected mistake bound framework introduced by Haussler, Littlestone and Warmuth (1987). The measure of performance we use is the expected difference between the total reward received by the learning agent and that received by an agent behaving optimally from the start. We call this expected difference the cumulative mistake of the agent and we require that it "levels off" at a reasonably fast rate as the learning progresses. We show that this model is polynomially equivalent to the PAC model of off-line reinforcement learning introduced in (Fiechter, 1994). In particular we show how an off-line PAC reinforcement learning algorithm can be transformed into an efficient on-line algorithm in a simple and practical way. An immediate consequence of this result is that the PAC algorithm for the general finite state-space reinforcement learning problem described in (Fiechter, 1994) can be transformed into a polynomial on-line al gorithm with guaranteed performances. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #35863. 	[Title] Associative Reinforcement Learning: Functions in k-DNF [Abstract] An agent that must learn to act in the world by trial and error faces the reinforcement 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 algorithms that can efficiently learn action maps that are expressible in k-DNF. The algorithms are compared with existing methods in empirical trials and are shown to have very good performance. Please summarise all the given information.

Paper #481073 has the title `PAC Adaptive Control of Linear Systems  
` and the abstract `We consider a special case of reinforcement learning where the environment can be described by a linear system. The states of the environment and the actions the agent can perform are represented by real vectors and the system dynamic is given by a linear equation with a stochastic component. The problem is equivalent to the so-called linear quadratic regulator problem studied in the optimal and adaptive control literature. We propose a learning algorithm for that problem and analyze it in a PAC learning framework. Unlike the algorithms in the adaptive control literature, our algorithm actively explores the environment to learn an accurate model of the system faster. We show that the control law produced by our algorithm has, with high probability, a value that is close to that of an optimal policy relative to the magnitude of the initial state of the system. The time taken by the algorithm is polynomial in the dimension n of the state-space and in the dimension r of the action-space when the ratio n=r is a constant.
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Question: Which category should Paper #481073 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #481073 has the title `PAC Adaptive Control of Linear Systems  
` and the abstract `We consider a special case of reinforcement learning where the environment can be described by a linear system. The states of the environment and the actions the agent can perform are represented by real vectors and the system dynamic is given by a linear equation with a stochastic component. The problem is equivalent to the so-called linear quadratic regulator problem studied in the optimal and adaptive control literature. We propose a learning algorithm for that problem and analyze it in a PAC learning framework. Unlike the algorithms in the adaptive control literature, our algorithm actively explores the environment to learn an accurate model of the system faster. We show that the control law produced by our algorithm has, with high probability, a value that is close to that of an optimal policy relative to the magnitude of the initial state of the system. The time taken by the algorithm is polynomial in the dimension n of the state-space and in the dimension r of the action-space when the ratio n=r is a constant.
`.
Question: Which category should Paper #481073 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Reinforcement_Learning