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Showing 1 to 3 of 3 for “"Multi-Task Reinforcement Learning"”.
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Multi-Task Reinforcement Learning: From Single-Agent to Multi-Agent Systems
… of the technology. The ability to develop these multi-task, multi-agent drone systems is limited by the lack of available training environments, as well as deficiencies of multi-task learning due to a phenomenon known as catastrophic forgetting. In this thesis, we present a set of simulation …
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Sample Complexity of Incremental Policy Gradient Methods for Solving Multi-Task Reinforcement Learning
We consider a multi-task learning problem, where an agent is presented a number of N reinforcement learning tasks. To solve this problem, we are interested in studying the gradient approach, which iteratively updates an estimate of the optimal policy using the gradients of the value functions. The …
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Computationally efficient Gaussian Process changepoint detection and regression
… when data are nonstationary, i.e. generated from multiple switching processes. Existing methods for GP regression over non-stationary data include clustering and change-point detection algorithms. However, these methods require significant computation, do not come with provable guarantees on …