In this work, we propose to apply trust region optimization to deep reinforcement learning using a recently proposed Kronecker-factored approximation to the curvature.

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

In this paper, we first observe that policies learned using InRL can overfit to the other agents' policies during training, failing to sufficiently generalize during execution. We introduce a new metric, joint-policy correlation, to quantify this effect.

Autonomous agents optimize the reward function we give them.

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

Humanity faces numerous problems of common-pool resource appropriation.

One use of such an estimate is to evaluate risk; e.g. is this patient likely to die if I do not

We model this setting as a multi-view decision process, which we use to formally analyze the positive effect of steering policies.

We introduce a new formulation of the Hidden Parameter Markov Decision Process (HiP-MDP), a framework for modeling families of related tasks using low-dimensional latent embeddings.

The intuition is that, if a state is easy to distinguish from other states, it is likely to be novel.