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Neural Topological Ordering for Computation Graphs
Recent works on machine learning for combinatorial optimization have shown that learning based approaches can outperform heuristic methods in terms of speed and performance.
A relaxed technical assumption for posterior sampling-based reinforcement learning for control of unknown linear systems
The regret bound of the algorithm was derived under a technical assumption on the induced norm of the closed loop system.
Thompson sampling for linear quadratic mean-field teams
We consider optimal control of an unknown multi-agent linear quadratic (LQ) system where the dynamics and the cost are coupled across the agents through the mean-field (i. e., empirical mean) of the states and controls.
Learning Unknown Markov Decision Processes: A Thompson Sampling Approach
This regret bound matches the best available bound for weakly communicating MDPs.
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