Fast Algorithms for $L_\infty$-constrained S-rectangular Robust MDPs

no code implementations • NeurIPS 2021 • Bahram Behzadian, Marek Petrik, Chin Pang Ho

Robust Markov decision processes (RMDPs) are a useful building block of robust reinforcement learning algorithms but can be hard to solve.

reinforcement-learning Reinforcement Learning (RL)

Entropic Risk Constrained Soft-Robust Policy Optimization

no code implementations • 20 Jun 2020 • Reazul Hasan Russel, Bahram Behzadian, Marek Petrik

Having a perfect model to compute the optimal policy is often infeasible in reinforcement learning.

reinforcement-learning Reinforcement Learning (RL)

Optimizing Norm-Bounded Weighted Ambiguity Sets for Robust MDPs

no code implementations • 4 Dec 2019 • Reazul Hasan Russel, Bahram Behzadian, Marek Petrik

Our proposed method computes a weight parameter from the value functions, and these weights then drive the shape of the ambiguity sets.

Optimizing Percentile Criterion Using Robust MDPs

no code implementations • 23 Oct 2019 • Bahram Behzadian, Reazul Hasan Russel, Marek Petrik, Chin Pang Ho

We then propose new algorithms that minimize the span of ambiguity sets defined by weighted $L_1$ and $L_\infty$ norms.

Reinforcement Learning (RL)

Monte Carlo Localization in Hand-Drawn Maps

no code implementations • 2 Apr 2015 • Bahram Behzadian, Pratik Agarwal, Wolfram Burgard, Gian Diego Tipaldi

In this paper, we address the localization problem when the map of the environment is not present beforehand, and the robot relies on a hand-drawn map from a non-expert user.