Search Results for author:
Found 8 papers, 3 papers with code
Vision-Language Models as a Source of Rewards
Building generalist agents that can accomplish many goals in rich open-ended environments is one of the research frontiers for reinforcement learning.
Finding Increasingly Large Extremal Graphs with AlphaZero and Tabu Search
This work studies a central extremal graph theory problem inspired by a 1975 conjecture of Erd\H{o}s, which aims to find graphs with a given size (number of nodes) that maximize the number of edges without having 3- or 4-cycles.
Learning how to Interact with a Complex Interface using Hierarchical Reinforcement Learning
While the native action space is completely intractable for simple DQN agents, our architecture can be used to establish an effective way to interact with different tasks, significantly improving the performance of the same DQN agent over different levels of abstraction.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Temporally Abstract Partial Models
Humans and animals have the ability to reason and make predictions about different courses of action at many time scales.
The Option Keyboard: Combining Skills in Reinforcement Learning
Building on this insight and on previous results on transfer learning, we show how to approximate options whose cumulants are linear combinations of the cumulants of known options.
AndroidEnv: A Reinforcement Learning Platform for Android
We introduce AndroidEnv, an open-source platform for Reinforcement Learning (RL) research built on top of the Android ecosystem.
What can I do here? A Theory of Affordances in Reinforcement Learning
Gibson (1977) coined the term "affordances" to describe the fact that certain states enable an agent to do certain actions, in the context of embodied agents.
Basis refinement strategies for linear value function approximation in MDPs
We provide a theoretical framework for analyzing basis function construction for linear value function approximation in Markov Decision Processes (MDPs).
