15 citations · 24 across the 5 of their papers we have counts for
8 papers
Deep RL Needs Deep Behavior Analysis: Exploring Implicit Planning by Model-Free Agents in Open-Ended Environments
Riley Simmons-Edler, Ryan P. Badman, Felix Baastad Berg +5
Understanding the behavior of deep reinforcement learning (DRL) agents -particularly as task and agent sophistication increase- requires more than simple comparison of reward curve…
Military AI Needs Technically-Informed Regulation to Safeguard AI Research and its Applications
Riley Simmons-Edler, Jean Dong, Paul Lushenko +2
Military weapon systems and command-and-control infrastructure augmented by artificial intelligence (AI) have seen rapid development and deployment in recent years. However, the so…
AI-Powered Autonomous Weapons Risk Geopolitical Instability and Threaten AI Research
Riley Simmons-Edler, Ryan Badman, Shayne Longpre +1
The recent embrace of machine learning (ML) in the development of autonomous weapons systems (AWS) creates serious risks to geopolitical stability and the free exchange of ideas in…
AuraSense: Robot Collision Avoidance by Full Surface Proximity Detection
Xiaoran Fan, Riley Simmons-Edler, Daewon Lee +3
Perceiving obstacles and avoiding collisions is fundamental to the safe operation of a robot system, particularly when the robot must operate in highly dynamic human environments.…
Towards Practical Credit Assignment for Deep Reinforcement Learning
Vyacheslav Alipov, Riley Simmons-Edler, Nikita Putintsev +2
Credit assignment is a fundamental problem in reinforcement learning, the problem of measuring an action's influence on future rewards. Explicit credit assignment methods have the…
Reward Prediction Error as an Exploration Objective in Deep RL
Riley Simmons-Edler, Ben Eisner, Daniel Yang +4
A major challenge in reinforcement learning is exploration, when local dithering methods such as epsilon-greedy sampling are insufficient to solve a given task. Many recent methods…
Q-Learning for Continuous Actions with Cross-Entropy Guided Policies
Riley Simmons-Edler, Ben Eisner, Eric Mitchell +2
Off-Policy reinforcement learning (RL) is an important class of methods for many problem domains, such as robotics, where the cost of collecting data is high and on-policy methods…
Program Synthesis Through Reinforcement Learning Guided Tree Search
Riley Simmons-Edler, Anders Miltner, Sebastian Seung
Program Synthesis is the task of generating a program from a provided specification. Traditionally, this has been treated as a search problem by the programming languages (PL) comm…