2 citations · 2 across the 8 of their papers we have counts for
9 papers
AI+HW 2035: Shaping the Next Decade
Deming Chen, Jason Cong, Azalia Mirhoseini +27
Artificial intelligence (AI) and hardware (HW) are advancing at unprecedented rates, yet their trajectories have become inseparably intertwined. The global research community lacks…
Knowledge Retention for Continual Model-Based Reinforcement Learning
Yixiang Sun, Haotian Fu, Michael Littman +1
We propose DRAGO, a novel approach for continual model-based reinforcement learning aimed at improving the incremental development of world models across a sequence of tasks that d…
Model-based Lifelong Reinforcement Learning with Bayesian Exploration
Haotian Fu, Shangqun Yu, Michael Littman +1
We propose a model-based lifelong reinforcement-learning approach that estimates a hierarchical Bayesian posterior distilling the common structure shared across different tasks. Th…
Meta-Learning Parameterized Skills
Haotian Fu, Shangqun Yu, Saket Tiwari +2
We propose a novel parameterized skill-learning algorithm that aims to learn transferable parameterized skills and synthesize them into a new action space that supports efficient l…
Does DQN really learn? Exploring adversarial training schemes in Pong
Bowen He, Sreehari Rammohan, Jessica Forde +1
In this work, we study two self-play training schemes, Chainer and Pool, and show they lead to improved agent performance in Atari Pong compared to a standard DQN agent -- trained…
Learning Generalizable Behavior via Visual Rewrite Rules
Yiheng Xie, Mingxuan Li, Shangqun Yu +1
Though deep reinforcement learning agents have achieved unprecedented success in recent years, their learned policies can be brittle, failing to generalize to even slight modificat…
Learning Finite Linear Temporal Logic Specifications with a Specialized Neural Operator
Homer Walke, Daniel Ritter, Carl Trimbach +1
Finite linear temporal logic () is a powerful formal representation for modeling temporal sequences. We address the problem of learning a compact f…
On the (In)Tractability of Reinforcement Learning for LTL Objectives
Cambridge Yang, Michael Littman, Michael Carbin
In recent years, researchers have made significant progress in devising reinforcement-learning algorithms for optimizing linear temporal logic (LTL) objectives and LTL-like objecti…
Coarse-Grained Smoothness for RL in Metric Spaces
Omer Gottesman, Kavosh Asadi, Cameron Allen +3
Principled decision-making in continuous state--action spaces is impossible without some assumptions. A common approach is to assume Lipschitz continuity of the Q-function. We show…