360 citations · 2.7k across the 150 of their papers we have counts for
25 papers · 1 filter
Improving Sample Efficiency of Value Based Models Using Attention and Vision Transformers
Amir Ardalan Kalantari, Mohammad Amini, Sarath Chandar +1
Much of recent Deep Reinforcement Learning success is owed to the neural architecture's potential to learn and use effective internal representations of the world. While many curre…
Proving Theorems using Incremental Learning and Hindsight Experience Replay
Eser Aygün, Laurent Orseau, Ankit Anand +5
Traditional automated theorem provers for first-order logic depend on speed-optimized search and many handcrafted heuristics that are designed to work best over a wide range of dom…
The Option Keyboard: Combining Skills in Reinforcement Learning
André Barreto, Diana Borsa, Shaobo Hou +8
The ability to combine known skills to create new ones may be crucial in the solution of complex reinforcement learning problems that unfold over extended periods. We argue that a…
A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning
Mingde Zhao, Zhen Liu, Sitao Luan +3
We present an end-to-end, model-based deep reinforcement learning agent which dynamically attends to relevant parts of its state during planning. The agent uses a bottleneck mechan…
Training a First-Order Theorem Prover from Synthetic Data
Vlad Firoiu, Eser Aygun, Ankit Anand +6
A major challenge in applying machine learning to automated theorem proving is the scarcity of training data, which is a key ingredient in training successful deep learning models.…
Algorithmic Improvements for Deep Reinforcement Learning applied to Interactive Fiction
Vishal Jain, William Fedus, Hugo Larochelle +2
Text-based games are a natural challenge domain for deep reinforcement learning algorithms. Their state and action spaces are combinatorially large, their reward function is sparse…