560 citations · 3.3k across the 60 of their papers we have counts for
64 papers
A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems
Megan M. Baker, Alexander New, Mario Aguilar-Simon +44
Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks enc…
Tiered Reward: Designing Rewards for Specification and Fast Learning of Desired Behavior
Zhiyuan Zhou, Shreyas Sundara Raman, Henry Sowerby +1
Reinforcement-learning agents seek to maximize a reward signal through environmental interactions. As humans, our job in the learning process is to design reward functions to expre…
Evaluation Beyond Task Performance: Analyzing Concepts in AlphaZero in Hex
Charles Lovering, Jessica Zosa Forde, George Konidaris +2
AlphaZero, an approach to reinforcement learning that couples neural networks and Monte Carlo tree search (MCTS), has produced state-of-the-art strategies for traditional board gam…
Reward-Predictive Clustering
Lucas Lehnert, Michael J. Frank, Michael L. Littman
Recent advances in reinforcement-learning research have demonstrated impressive results in building algorithms that can out-perform humans in complex tasks. Nevertheless, creating…
Gathering Strength, Gathering Storms: The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report
Michael L. Littman, Ifeoma Ajunwa, Guy Berger +14
In September 2021, the "One Hundred Year Study on Artificial Intelligence" project (AI100) issued the second report of its planned long-term periodic assessment of artificial intel…
Designing Rewards for Fast Learning
Henry Sowerby, Zhiyuan Zhou, Michael L. Littman
To convey desired behavior to a Reinforcement Learning (RL) agent, a designer must choose a reward function for the environment, arguably the most important knob designers have in…