Bahram Behzadian

Research Scientist, Meta

About

I work on reinforcement learning and sequential decision-making, with a focus on reasoning, planning, and long-horizon decision problems.

My background spans robust MDPs, learning under uncertainty, and partial observability, including scalable robust RL methods and large-scale online learning systems. I study how learning dynamics, representation, and planning interact to produce reliable behavior in complex environments.

Research Interests

  • RL for reasoning and planning in long-horizon decision problems
  • Robust RL and learning under uncertainty (robust MDPs, ambiguity sets)
  • Partial observability and stable learning dynamics
  • Value-/policy-iteration style methods and hybrid planning–learning approaches

Selected Work

Fast Algorithms for L∞-Constrained S-Rectangular Robust MDPs NeurIPS 2021 · Bahram Behzadian, Marek Petrik, Chin Pang Ho Scalable robust policy computation for structured ambiguity sets. [PDF]

Optimizing Percentile Criterion using Robust MDPs AISTATS 2021 · Bahram Behzadian, Reazul Hasan Russel, Marek Petrik, Chin Pang Ho Robust decision-making under risk/quantile objectives. [PDF]

Fast Feature Selection for Linear Value Function Approximation ICAPS 2019 · Bahram Behzadian, Soheil Gharatappeh, Marek Petrik Representation/feature selection for value-based RL. [PDF]

Monte Carlo Localization in Hand-Drawn Maps IROS 2015 · Bahram Behzadian, Pratik Agarwal, Wolfram Burgard, Gian Diego Tipaldi State estimation under nonstandard/noisy map representations. [PDF]

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