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Gilad Adler

2 accepted papers

2024

MAMBA: an Effective World Model Approach for Meta-Reinforcement Learning

ICLR 2024poster

Meta-reinforcement learning (meta-RL) is a promising framework for tackling challenging domains requiring efficient exploration. Existing meta-RL algorithms are characterized by low sample efficiency, and mostly focus on low-dimensional task distributions. In parallel, model-based RL methods have be…

2022

Meta Reinforcement Learning with Finite Training Tasks - a Density Estimation Approach

NeurIPS 2022accept

In meta reinforcement learning (meta RL), an agent learns from a set of training tasks how to quickly solve a new task, drawn from the same task distribution. The optimal meta RL policy, a.k.a.~the Bayes-optimal behavior, is well defined, and guarantees optimal reward in expectation, taken with resp…