2,596 research outputs found
Model-based Reinforcement Learning and the Eluder Dimension
We consider the problem of learning to optimize an unknown Markov decision
process (MDP). We show that, if the MDP can be parameterized within some known
function class, we can obtain regret bounds that scale with the dimensionality,
rather than cardinality, of the system. We characterize this dependence
explicitly as where is time elapsed, is
the Kolmogorov dimension and is the \emph{eluder dimension}. These
represent the first unified regret bounds for model-based reinforcement
learning and provide state of the art guarantees in several important settings.
Moreover, we present a simple and computationally efficient algorithm
\emph{posterior sampling for reinforcement learning} (PSRL) that satisfies
these bounds
Online Optimization with Memory and Competitive Control
This paper presents competitive algorithms for a novel class of online optimization problems with memory. We consider a setting where the learner seeks to minimize the sum of a hitting cost and a switching cost that depends on the previous p decisions. This setting generalizes Smoothed Online Convex Optimization. The proposed approach, Optimistic Regularized Online Balanced Descent, achieves a constant, dimension-free competitive ratio. Further, we show a connection between online optimization with memory and online control with adversarial disturbances. This connection, in turn, leads to a new constant-competitive policy for a rich class of online control problems
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