Search Results for author: Ramtin Keramati

Found 7 papers, 2 papers with code

Identification of Subgroups With Similar Benefits in Off-Policy Policy Evaluation

no code implementations28 Nov 2021 Ramtin Keramati, Omer Gottesman, Leo Anthony Celi, Finale Doshi-Velez, Emma Brunskill

Off-policy policy evaluation methods for sequential decision making can be used to help identify if a proposed decision policy is better than a current baseline policy.

Decision Making

Learning Abstract Models for Strategic Exploration and Fast Reward Transfer

1 code implementation12 Jul 2020 Evan Zheran Liu, Ramtin Keramati, Sudarshan Seshadri, Kelvin Guu, Panupong Pasupat, Emma Brunskill, Percy Liang

Model-based reinforcement learning (RL) is appealing because (i) it enables planning and thus more strategic exploration, and (ii) by decoupling dynamics from rewards, it enables fast transfer to new reward functions.

Model-based Reinforcement Learning Montezuma's Revenge +1

Value Driven Representation for Human-in-the-Loop Reinforcement Learning

no code implementations2 Apr 2020 Ramtin Keramati, Emma Brunskill

In such systems there is typically an external human system designer that is creating, monitoring and modifying the interactive adaptive system, trying to improve its performance on the target outcomes.

reinforcement-learning

Off-policy Policy Evaluation For Sequential Decisions Under Unobserved Confounding

1 code implementation NeurIPS 2020 Hongseok Namkoong, Ramtin Keramati, Steve Yadlowsky, Emma Brunskill

We assess robustness of OPE methods under unobserved confounding by developing worst-case bounds on the performance of an evaluation policy.

Decision Making Management

Being Optimistic to Be Conservative: Quickly Learning a CVaR Policy

no code implementations5 Nov 2019 Ramtin Keramati, Christoph Dann, Alex Tamkin, Emma Brunskill

While maximizing expected return is the goal in most reinforcement learning approaches, risk-sensitive objectives such as conditional value at risk (CVaR) are more suitable for many high-stakes applications.

Learning Abstract Models for Long-Horizon Exploration

no code implementations ICLR 2019 Evan Zheran Liu, Ramtin Keramati, Sudarshan Seshadri, Kelvin Guu, Panupong Pasupat, Emma Brunskill, Percy Liang

In our approach, a manager maintains an abstract MDP over a subset of the abstract states, which grows monotonically through targeted exploration (possible due to the abstract MDP).

Atari Games

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