1.3k citations · 4.8k across the 58 of their papers we have counts for
12 papers · 1 filter
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…
Benchmarking Bonus-Based Exploration Methods on the Arcade Learning Environment
Adrien Ali Taïga, William Fedus, Marlos C. Machado +2
This paper provides an empirical evaluation of recently developed exploration algorithms within the Arcade Learning Environment (ALE). We study the use of different reward bonuses…
DeepMDP: Learning Continuous Latent Space Models for Representation Learning
Carles Gelada, Saurabh Kumar, Jacob Buckman +2
Many reinforcement learning (RL) tasks provide the agent with high-dimensional observations that can be simplified into low-dimensional continuous states. To formalize this process…
Hyperbolic Discounting and Learning over Multiple Horizons
William Fedus, Carles Gelada, Yoshua Bengio +2
Reinforcement learning (RL) typically defines a discount factor as part of the Markov Decision Process. The discount factor values future rewards by an exponential scheme that lead…
Statistics and Samples in Distributional Reinforcement Learning
Mark Rowland, Robert Dadashi, Saurabh Kumar +3
We present a unifying framework for designing and analysing distributional reinforcement learning (DRL) algorithms in terms of recursively estimating statistics of the return distr…
A Comparative Analysis of Expected and Distributional Reinforcement Learning
Clare Lyle, Pablo Samuel Castro, Marc G. Bellemare
Since their introduction a year ago, distributional approaches to reinforcement learning (distributional RL) have produced strong results relative to the standard approach which mo…
Distributional reinforcement learning with linear function approximation
Marc G. Bellemare, Nicolas Le Roux, Pablo Samuel Castro +1
Despite many algorithmic advances, our theoretical understanding of practical distributional reinforcement learning methods remains limited. One exception is Rowland et al. (2018)'…
The Hanabi Challenge: A New Frontier for AI Research
Nolan Bard, Jakob N. Foerster, Sarath Chandar +12
From the early days of computing, games have been important testbeds for studying how well machines can do sophisticated decision making. In recent years, machine learning has made…
Shaping the Narrative Arc: An Information-Theoretic Approach to Collaborative Dialogue
Kory W. Mathewson, Pablo Samuel Castro, Colin Cherry +2
We consider the problem of designing an artificial agent capable of interacting with humans in collaborative dialogue to produce creative, engaging narratives. In this task, the go…
Off-Policy Deep Reinforcement Learning by Bootstrapping the Covariate Shift
Carles Gelada, Marc G. Bellemare
In this paper we revisit the method of off-policy corrections for reinforcement learning (COP-TD) pioneered by Hallak et al. (2017). Under this method, online updates to the value…
A Geometric Perspective on Optimal Representations for Reinforcement Learning
Marc G. Bellemare, Will Dabney, Robert Dadashi +6
We propose a new perspective on representation learning in reinforcement learning based on geometric properties of the space of value functions. We leverage this perspective to pro…
The Value Function Polytope in Reinforcement Learning
Robert Dadashi, Adrien Ali Taïga, Nicolas Le Roux +2
We establish geometric and topological properties of the space of value functions in finite state-action Markov decision processes. Our main contribution is the characterization of…