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We present new algorithms for reinforcement learning, and prove that they have polynomial bounds on the resources required to achieve near-optimal return in general Markov decision processes. After observing that the number of actions required to approach the optimal return is lower bounded by the mixing time T of the optimal policy (in the undiscounted… (More)

An issue that is critical for the application of Markov decision processes (MDPs) to realistic problems is how the complexity of planning scales with the size of the MDP. In stochas-tic environments with very large or even inn-nite state spaces, traditional planning and reinforcement learning algorithms are often in-applicable, since their running time… (More)

No other new books crossed my desk, although Mark Weiss, our Technical Report Column organizer for SIGA CT News has threatened to send me his latest book. It will likely be here in time for the next issue of the News. Logout From Bozeman, where once again the howl of the wolf will be heard among the forests and meadows of the Greater Yellowstone… (More)

We introduce a compact graph-theoretic representation for multi-party game theory. Our main result is a provably correct and efficient algorithm for computing approximate Nash equilibria in one-stage games represented by trees or sparse graphs.

In this paper, we study the problem of learning in the presence of classification noise in the probabilistic learning model of Valiant and its variants. In order to identify the class of “robust” learning algorithms in the most general way, we formalize a new but related model of learning from <italic>statistical queries</italic>. Intuitively,… (More)

In this paper we initiate an investigation of generalizations of the Probably Approximately Correct (PAC) learning model that attempt to significantly weaken the target function assumptions. The ultimate goal in this direction is informally termed <italic>agnostic learning</italic>, in which we make virtually no assumptions on the target function. The name… (More)

In this paper, we prove the intractability of learning several classes of Boolean functions in the distribution-free model (also called the Probably Approximately Correct or PAC model) of learning from examples. These results are <italic>representation independent</italic>, in that they hold regardless of the syntactic form in which the learner chooses to… (More)

While most theoretical work in machine learning has focused on the complexity of learning, recently there has been increasing interest in formally studying the complexity o f teaching. In this paper we study the complexity of teaching by considering a variant of the on-line learning model in which a helpful teacher selects the instances. We measure the… (More)

Multi-agent games are becoming an increasingly prevalent formalism for the study of electronic commerce and auctions. The speed at which transactions can take place and the growing complexity of electronic marketplaces makes the study of computationally simple agents an appealing direction. In this work, we analyze the behavior of agents that incrementally… (More)

In this paper we investigate a new formal model of machine learning in which the concept (boolean function) to be learned may exhibit uncertain or probabilistic behavior|thus, the same input may sometimes be classiied as a positive example and sometimes as a negative example. Such probabilistic concepts (or p-concepts) may arise in situations such as… (More)