439 citations · 503 across the 16 of their papers we have counts for
9 papers · 1 filter
Composing Task Knowledge with Modular Successor Feature Approximators
Wilka Carvalho, Angelos Filos, Richard L. Lewis +2
Recently, the Successor Features and Generalized Policy Improvement (SF&GPI) framework has been proposed as a method for learning, composing, and transferring predictive knowledge…
Large Language Models can Implement Policy Iteration
Ethan Brooks, Logan Walls, Richard L. Lewis +1
This work presents In-Context Policy Iteration, an algorithm for performing Reinforcement Learning (RL), in-context, using foundation models. While the application of foundation mo…
GrASP: Gradient-Based Affordance Selection for Planning
Vivek Veeriah, Zeyu Zheng, Richard Lewis +1
Planning with a learned model is arguably a key component of intelligence. There are several challenges in realizing such a component in large-scale reinforcement learning (RL) pro…
Reinforcement Learning of Implicit and Explicit Control Flow in Instructions
Ethan A. Brooks, Janarthanan Rajendran, Richard L. Lewis +1
Learning to flexibly follow task instructions in dynamic environments poses interesting challenges for reinforcement learning agents. We focus here on the problem of learning contr…
Adaptive Pairwise Weights for Temporal Credit Assignment
Zeyu Zheng, Risto Vuorio, Richard Lewis +1
How much credit (or blame) should an action taken in a state get for a future reward? This is the fundamental temporal credit assignment problem in Reinforcement Learning (RL). One…
Learning State Representations from Random Deep Action-conditional Predictions
Zeyu Zheng, Vivek Veeriah, Risto Vuorio +2
Our main contribution in this work is an empirical finding that random General Value Functions (GVFs), i.e., deep action-conditional predictions -- random both in what feature of o…
Reinforcement Learning for Sparse-Reward Object-Interaction Tasks in a First-person Simulated 3D Environment
Wilka Carvalho, Anthony Liang, Kimin Lee +4
First-person object-interaction tasks in high-fidelity, 3D, simulated environments such as the AI2Thor virtual home-environment pose significant sample-efficiency challenges for re…
Action-Conditional Video Prediction using Deep Networks in Atari Games
Junhyuk Oh, Xiaoxiao Guo, Honglak Lee +2
Motivated by vision-based reinforcement learning (RL) problems, in particular Atari games from the recent benchmark Aracade Learning Environment (ALE), we consider spatio-temporal…
Variance-Based Rewards for Approximate Bayesian Reinforcement Learning
Jonathan Sorg, Satinder Singh, Richard L. Lewis
The explore{exploit dilemma is one of the central challenges in Reinforcement Learning (RL). Bayesian RL solves the dilemma by providing the agent with information in the form of a…