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Learning to Defend by Attacking (and Vice-Versa): Transfer of Learning in Cybersecurity Games
Designing cyber defense systems to account for cognitive biases in human decision making has demonstrated significant success in improving performance against human attackers.
Learning in Factored Domains with Information-Constrained Visual Representations
This raises the question of how humans learn to efficiently represent visual information in a manner useful for learning tasks.
Consolidation via Policy Information Regularization in Deep RL for Multi-Agent Games
This paper introduces an information-theoretic constraint on learned policy complexity in the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) reinforcement learning algorithm.
Deep RL With Information Constrained Policies: Generalization in Continuous Control
We focus on the model-free reinforcement learning (RL) setting and formalize our approach in terms of an information-theoretic constraint on the complexity of learned policies.
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