Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 9 of 9 for “"temporal difference learning"”.
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Explorations of the practical issues of learning prediction-control tasks using temporal difference learning methods
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1993.
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Sparse Value Function Approximation for Reinforcement Learning
<p>A key component of many reinforcement learning (RL) algorithms is the approximation of the value function. The design and selection of features for approximation in RL is crucial, and an ongoing area of research. One approach to the problem of feature selection is to apply sparsity-inducing …
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Efficient reinforcement learning via singular value decomposition, end-to-end model-based methods and reward shaping
Reinforcement learning (RL) provides a general framework for data-driven decision making. However, the very same generality that makes this approach applicable to a wide range of problems is also responsible for its well-known inefficiencies. In this thesis, we consider different properties which …
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Learning optimal discourse strategies in a spoken dialogue system
… thesis investigates the issues involved with learning optimal discourse strategies on the basis of experience gained through conversations between human users and natural language agents. A spoken dialogue agent, ELVIS, is implemented as a testbed for learning optimal discourse strategies. …
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The Impact of Threat on Behavioral and Neural Markers of Learning in Anxiety
… Decision science and in particular reinforcement learning models provide a quantitative framework to explain how the likelihood and value of such outcomes are estimated, thus allowing the measurement of parameters of decision-making that may differ between high- and low- anxiety groups. However, …
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Designing policy optimization algorithms for multi-agent reinforcement learning
Multi-agent reinforcement learning (RL) studies the sequential decision-making problem in the setting where multiple agents exist in an environment and jointly determine the environment transition. The relationship between the agents can be cooperative, competitive, or mixed depending on how the …
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Models of aposematism and the role of aversive learning
… of co-evolution and the mechanisms of aversive learning are at the heart of the current research. On the one hand, to explain stability and persistence of aposematic signals requires a theory of co-evolution of defence and signals. On the other hand, the role of the predator and details of the …
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Evolving Neural Networks with HyperNEAT and Online Training
… through strategic application of online learning. Several methods are proposed and explored. All methodologies are tested using a team gathering task. A simulated environment is setup with gathering robots that must locate resources and work together to carry the resources back to a …
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Hraní nedeterministických her s učením
Práce se věnuje studiu a implementaci metod použitých pro učení z průběhu hraní. Zvolenou hrou pro tuhle práci jsou Vrhcáby. Algoritmus použitý pro učení neuronové sítě se nazývá učení z časového rozdílu s použitím stop vhodnosti. Tento algoritmus je známý i pod jménem TD(lambda). V teoretické …