We present new algorithms for inverse opti- mal control (or inverse reinforcement learn- ing, IRL) within the framework of linearly- solvable MDPs (LMDPs).
Abstract. We present new algorithms for inverse optimal con- trol (or inverse reinforcement learning, IRL) within the framework of linearly-solvable MDPs.
We present new algorithms for inverse optimal control (or inverse reinforcement learning, IRL) within the framework of linearly-solvable MDPs (LMDPs).
The likelihood of observing a set of transitions under optimal control for the MDP, ... Inverse optimal control with linearly-solvable mdps. ICML'10, 335–342 ...
May 5, 2016 · Linearly solvable MDPs are a continuous relaxation of normal MDPs. The differ from normal MDPs in that they don't use a traditional notion of an action ...
Mar 17, 2015 · We present new algorithms for inverse opti- mal control (or inverse reinforcement learn- ing, IRL) within the framework of linearly- ...
Further, we show that the Inverse Optimal Control problem, that is, the problem of inferring the cost function given trajectories sampled from the optimal ...
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In this paper, we present an IOC algorithm that efficiently handles deterministic MDPs with large, continuous state and action spaces by con- sidering only the ...
This paper reviews the history of the IOC and Inverse Reinforcement Learning (IRL) approaches and describes the connections and differences between them.
We summarize the recently-developed framework of linearly-solvable stochastic op- timal control. Using an exponential transformation, the (Hamilton-Jacobi) ...