Strategic Object Oriented Reinforcement Learning

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COBRA: Data-Efficient Model-Based RL through Unsupervised Object Discovery and Curiosity-Driven Exploration (2019, background): “, 2008) promises data efficiency and generalization by leveraging representations of objects and their relations (Keramati et al., 2018).”
Reasoning About Physical Interactions with Object-Oriented Prediction and Planning (2018, background): “Object-centric scene decompositions have also been considered as a potential state representation in reinforcement learning (Diuk et al., 2008; Scholz et al., 2014; Devin et al., 2017; Goel et al., 2018; Keramati et al., 2018).”
Hierarchical reinforcement learning for efficient exploration and transfer (2020, background): “Keramati et al. (2018) propose a model-based framework to solve sparse-reward domains, and incorporate macro-actions in the form of ﬁxed action sequences that can be selected as a single decision.”
Toybox: A Suite of Environments for Experimental Evaluation of Deep Reinforcement Learning (2019, background): “Models trained on a compact representation of state can train more quickly (Keramati et al., 2018; Melnik et al., 2018), but the underlying environment dynamics have not changed.”