27 citations · 55 across the 5 of their papers we have counts for
12 papers
Temporal Abstractions-Augmented Temporally Contrastive Learning: An Alternative to the Laplacian in RL
Akram Erraqabi, Marlos C. Machado, Mingde Zhao +4
In reinforcement learning, the graph Laplacian has proved to be a valuable tool in the task-agnostic setting, with applications ranging from skill discovery to reward shaping. Rece…
On Bonus-Based Exploration Methods in the Arcade Learning Environment
Adrien Ali Taïga, William Fedus, Marlos C. Machado +2
Research on exploration in reinforcement learning, as applied to Atari 2600 game-playing, has emphasized tackling difficult exploration problems such as Montezuma's Revenge (Bellem…
Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning
Rishabh Agarwal, Marlos C. Machado, Pablo Samuel Castro +1
Reinforcement learning methods trained on few environments rarely learn policies that generalize to unseen environments. To improve generalization, we incorporate the inherent sequ…
Beyond variance reduction: Understanding the true impact of baselines on policy optimization
Wesley Chung, Valentin Thomas, Marlos C. Machado +1
Bandit and reinforcement learning (RL) problems can often be framed as optimization problems where the goal is to maximize average performance while having access only to stochasti…
An operator view of policy gradient methods
Dibya Ghosh, Marlos C. Machado, Nicolas Le Roux
We cast policy gradient methods as the repeated application of two operators: a policy improvement operator , which maps any policy to a better one ,…
Generalization and Regularization in DQN
Jesse Farebrother, Marlos C. Machado, Michael Bowling
Deep reinforcement learning algorithms have shown an impressive ability to learn complex control policies in high-dimensional tasks. However, despite the ever-increasing performanc…