153 citations · 387 across the 30 of their papers we have counts for
6 papers · 2 filters
Escape Room: A Configurable Testbed for Hierarchical Reinforcement Learning
Jacob Menashe, Peter Stone
Recent successes in Reinforcement Learning have encouraged a fast-growing network of RL researchers and a number of breakthroughs in RL research. As the RL community and the body o…
Robot Representation and Reasoning with Knowledge from Reinforcement Learning
Keting Lu, Shiqi Zhang, Peter Stone +1
Reinforcement learning (RL) agents aim at learning by interacting with an environment, and are not designed for representing or reasoning with declarative knowledge. Knowledge repr…
Deterministic Implementations for Reproducibility in Deep Reinforcement Learning
Prabhat Nagarajan, Garrett Warnell, Peter Stone
While deep reinforcement learning (DRL) has led to numerous successes in recent years, reproducing these successes can be extremely challenging. One reproducibility challenge parti…
A Century Long Commitment to Assessing Artificial Intelligence and its Impact on Society
Barbara J. Grosz, Peter Stone
In September 2016, Stanford's "One Hundred Year Study on Artificial Intelligence" project (AI100) issued the first report of its planned long-term periodic assessment of artificial…
Behavioral Cloning from Observation
Faraz Torabi, Garrett Warnell, Peter Stone
Humans often learn how to perform tasks via imitation: they observe others perform a task, and then very quickly infer the appropriate actions to take based on their observations.…
Task Planning in Robotics: an Empirical Comparison of PDDL-based and ASP-based Systems
Yuqian Jiang, Shiqi Zhang, Piyush Khandelwal +1
Robots need task planning algorithms to sequence actions toward accomplishing goals that are impossible through individual actions. Off-the-shelf task planners can be used by intel…