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20172022
most citedPerceptual Values from Observation

2 citations · 4 across the 3 of their papers we have counts for

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6 papers · 1 filter

cs.LG20222 cited

Learning Robust Real-Time Cultural Transmission without Human Data

Cultural General Intelligence Team, Avishkar Bhoopchand, Bethanie Brownfield +16

Cultural transmission is the domain-general social skill that allows agents to acquire and use information from each other in real-time with high fidelity and recall. In humans, it…

cs.LG2020

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…

cs.LG20192 cited

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…

cs.LG2018

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…

cs.LG2018

Forward-Backward Reinforcement Learning

Ashley D. Edwards, Laura Downs, James C. Davidson

Goals for reinforcement learning problems are typically defined through hand-specified rewards. To design such problems, developers of learning algorithms must inherently be aware…

cs.LG2017

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…