Implicit feedback is widely used in collaborative filtering methods for recommendation.

Recent work on generating adversarial attacks have shown that it is computationally feasible for a bad actor to fool a DRL policy into behaving sub optimally.

What policy should be employed in a Markov decision process with uncertain parameters? Robust optimization's answer to this question is to use rectangular

It extends POMDPs to multi-agent settings by including models of other agents in the state space and forming a hierarchical belief structure. In order to predict other agents' actions using I-POMDPs, we

Harsanyi's theorem shows that when the principals have a

However, many real-world problems inherently have multiple goals, where multi-objective formulations are more natural.

Neural Information Processing Systems http://nips.cc/

In many real-world reinforcement learning (RL) problems, besides optimizing the main objective function, an agent must concurrently avoid violating a number of constraints. In particular, besides optimizing performance, it is crucial to guarantee the safety of an agent during training as well as deployment (e.g., a robot should avoid taking actions - exploratory or not - which irrevocably harm its hardware). To incorporate safety in RL, we derive algorithms under the framework of constrained Markov decision processes (CMDPs), an extension of the standard Markov decision processes (MDPs) augmented with constraints on expected cumulative costs.

Neural Information Processing Systems http://nips.cc/

We present a new algorithm to generate minimal, stable, and symbolic corrections to an input that will cause a neural network with ReLU activations to change its output.