15 citations · 24 across the 5 of their papers we have counts for
3 papers · 1 filter
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…
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…