24 citations · 29 across the 22 of their papers we have counts for
5 papers · 1 filter
Compositional Instruction Following with Language Models and Reinforcement Learning
Vanya Cohen, Geraud Nangue Tasse, Nakul Gopalan +4
Combining reinforcement learning with language grounding is challenging as the agent needs to explore the environment while simultaneously learning multiple language-conditioned ta…
Efficient Exploration via First-Person Behavior Cloning Assisted Rapidly-Exploring Random Trees
Max Zuo, Logan Schick, Matthew Gombolay +1
Modern day computer games have extremely large state and action spaces. To detect bugs in these games' models, human testers play the games repeatedly to explore the game and find…
Learning to Follow Language Instructions with Compositional Policies
Vanya Cohen, Geraud Nangue Tasse, Nakul Gopalan +3
We propose a framework that learns to execute natural language instructions in an environment consisting of goal-reaching tasks that share components of their task descriptions. Ou…
Natural Language Specification of Reinforcement Learning Policies through Differentiable Decision Trees
Pradyumna Tambwekar, Andrew Silva, Nakul Gopalan +1
Human-AI policy specification is a novel procedure we define in which humans can collaboratively warm-start a robot's reinforcement learning policy. This procedure is comprised of…
Mitigating Planner Overfitting in Model-Based Reinforcement Learning
Dilip Arumugam, David Abel, Kavosh Asadi +5
An agent with an inaccurate model of its environment faces a difficult choice: it can ignore the errors in its model and act in the real world in whatever way it determines is opti…