465 citations · 4.1k across the 97 of their papers we have counts for
10 papers · 1 filter
Adversarial Motion Priors Make Good Substitutes for Complex Reward Functions
Alejandro Escontrela, Xue Bin Peng, Wenhao Yu +4
Training a high-dimensional simulated agent with an under-specified reward function often leads the agent to learn physically infeasible strategies that are ineffective when deploy…
AvE: Assistance via Empowerment
Yuqing Du, Stas Tiomkin, Emre Kiciman +3
One difficulty in using artificial agents for human-assistive applications lies in the challenge of accurately assisting with a person's goal(s). Existing methods tend to rely on i…
Hallucinative Topological Memory for Zero-Shot Visual Planning
Kara Liu, Thanard Kurutach, Christine Tung +2
In visual planning (VP), an agent learns to plan goal-directed behavior from observations of a dynamical system obtained offline, e.g., images obtained from self-supervised robot i…
Addressing Sample Complexity in Visual Tasks Using HER and Hallucinatory GANs
Himanshu Sahni, Toby Buckley, Pieter Abbeel +1
Reinforcement Learning (RL) algorithms typically require millions of environment interactions to learn successful policies in sparse reward settings. Hindsight Experience Replay (H…
Modular Architecture for StarCraft II with Deep Reinforcement Learning
Dennis Lee, Haoran Tang, Jeffrey O Zhang +3
We present a novel modular architecture for StarCraft II AI. The architecture splits responsibilities between multiple modules that each control one aspect of the game, such as bui…
Variational Option Discovery Algorithms
Joshua Achiam, Harrison Edwards, Dario Amodei +1
We explore methods for option discovery based on variational inference and make two algorithmic contributions. First: we highlight a tight connection between variational option dis…