Explaining RL Decisions with Trajectories
Explanation is a key component for the adoption of reinforcement learning (RL) in many real-world decision-making problems. In the …
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Robustness and Sample Complexity of Model-Based MARL for General Sum Markov Games
Multi-agent reinforcement learning (MARL) is often modeled using the framework of Markov games (also called stochastic games or dynamic …
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SARC: Soft Actor Retrospective Critic
The two-time scale nature of SAC, which is an actor-critic algorithm, is characterised by the fact that the critic estimate has not …
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Status-quo policy gradient in Multi-Agent Reinforcement Learning
Individual rationality, which involves maximizing expected individual returns, does not always lead to high-utility individual or group …
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Medical Dead-ends and Learning to Identify High-Risk States and Treatments
Machine learning has successfully framed many sequential decision making problems as either supervised prediction, or optimal …
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 An Empirical Study of Representation Learning for Reinforcement Learning in Healthcare
Reinforcement Learning (RL) has recently been applied to sequential estimation and prediction problems identifying and developing …
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Explain Your Move: Understanding Agent Actions Using Specific and Relevant Feature Attribution
As deep reinforcement learning (RL) is applied to more tasks, there is a need to visualize and understand the behavior of learned …
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Towards A Unified Framework for Visual Compatibility Prediction
Visual compatibility prediction refers to the task of determining if a set of items go well together. Existing techniques for …
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Powering Robust Fashion Retrieval with Information Rich Feature Embeddings
Visual content based product retrieval has become increasingly important for e-commerce. Fashion retrieval, in particular, is a …
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