831 citations · 3.2k across the 65 of their papers we have counts for
5 papers · 1 filter
Inverse reinforcement learning for video games
Aaron Tucker, Adam Gleave, Stuart Russell
Deep reinforcement learning achieves superhuman performance in a range of video game environments, but requires that a designer manually specify a reward function. It is often easi…
Learning and Planning with a Semantic Model
Yi Wu, Yuxin Wu, Aviv Tamar +3
Building deep reinforcement learning agents that can generalize and adapt to unseen environments remains a fundamental challenge for AI. This paper describes progresses on this cha…
Learning Plannable Representations with Causal InfoGAN
Thanard Kurutach, Aviv Tamar, Ge Yang +2
In recent years, deep generative models have been shown to 'imagine' convincing high-dimensional observations such as images, audio, and even video, learning directly from raw data…
An Efficient, Generalized Bellman Update For Cooperative Inverse Reinforcement Learning
Dhruv Malik, Malayandi Palaniappan, Jaime F. Fisac +3
Our goal is for AI systems to correctly identify and act according to their human user's objectives. Cooperative Inverse Reinforcement Learning (CIRL) formalizes this value alignme…
Discrete-Continuous Mixtures in Probabilistic Programming: Generalized Semantics and Inference Algorithms
Yi Wu, Siddharth Srivastava, Nicholas Hay +2
Despite the recent successes of probabilistic programming languages (PPLs) in AI applications, PPLs offer only limited support for random variables whose distributions combine disc…