55 citations · 99 across the 8 of their papers we have counts for
8 papers
Vision-Language Models as a Source of Rewards
Kate Baumli, Satinder Baveja, Feryal Behbahani +24
Building generalist agents that can accomplish many goals in rich open-ended environments is one of the research frontiers for reinforcement learning. A key limiting factor for bui…
In-context Reinforcement Learning with Algorithm Distillation
Michael Laskin, Luyu Wang, Junhyuk Oh +11
We propose Algorithm Distillation (AD), a method for distilling reinforcement learning (RL) algorithms into neural networks by modeling their training histories with a causal seque…
Hard Attention Control By Mutual Information Maximization
Himanshu Sahni, Charles Isbell
Biological agents have adopted the principle of attention to limit the rate of incoming information from the environment. One question that arises is if an artificial agent has acc…
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…
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…
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…
Learning to Compose Skills
Himanshu Sahni, Saurabh Kumar, Farhan Tejani +1
We present a differentiable framework capable of learning a wide variety of compositions of simple policies that we call skills. By recursively composing skills with themselves, we…
State Space Decomposition and Subgoal Creation for Transfer in Deep Reinforcement Learning
Himanshu Sahni, Saurabh Kumar, Farhan Tejani +2
Typical reinforcement learning (RL) agents learn to complete tasks specified by reward functions tailored to their domain. As such, the policies they learn do not generalize even t…