Publications
My research sits at the intersection of causality and reinforcement learning — understanding how causal reasoning can make RL agents more robust, efficient, and interpretable. Find my full profile on Google Scholar.
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2023Introduces an algorithm for active learning during model-based planning, combining active inference with model-based RL to improve sample efficiency and uncertainty-aware decision-making.
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2024MSc thesis surveying the current landscape of causal methods applied to reinforcement learning — covering key open problems and promising research directions.
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2021Reviews the state of causal reasoning in multi-agent settings and identifies the key open problems at the frontier of causal MARL research.
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2021Introduces Mava, a set of MARL components and system abstractions built on top of dm-acme for scalable, distributed training across hardware accelerators. Work done at InstaDeep. I contributed to the original system design and implemented the QMIX algorithm. A revised version of the paper and framework was released in 2023.
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2021Surveys counterfactual reasoning methods through the lens of Pearl's causal hierarchy, focusing on their application to sequential decision-making and RL.
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2021Surveys model-based RL approaches that use learned world models for planning, exploration, and sample-efficient learning in complex environments.