20 citations · 45 across the 9 of their papers we have counts for
5 papers · 1 filter
Universal Value Density Estimation for Imitation Learning and Goal-Conditioned Reinforcement Learning
Yannick Schroecker, Charles Isbell
This work considers two distinct settings: imitation learning and goal-conditioned reinforcement learning. In either case, effective solutions require the agent to reliably reach a…
Estimating Q(s,s') with Deep Deterministic Dynamics Gradients
Ashley D. Edwards, Himanshu Sahni, Rosanne Liu +7
In this paper, we introduce a novel form of value function, , that expresses the utility of transitioning from a state to a neighboring state and then acting opt…
Perceptual Values from Observation
Ashley D. Edwards, Charles L. Isbell
Imitation by observation is an approach for learning from expert demonstrations that lack action information, such as videos. Recent approaches to this problem can be placed into t…
Imitating Latent Policies from Observation
Ashley D. Edwards, Himanshu Sahni, Yannick Schroecker +1
In this paper, we describe a novel approach to imitation learning that infers latent policies directly from state observations. We introduce a method that characterizes the causal…
Transferring Agent Behaviors from Videos via Motion GANs
Ashley D. Edwards, Charles L. Isbell
A major bottleneck for developing general reinforcement learning agents is determining rewards that will yield desirable behaviors under various circumstances. We introduce a gener…