75 citations · 820 across the 81 of their papers we have counts for
6 papers · 1 filter
No-Regret Exploration in Goal-Oriented Reinforcement Learning
Jean Tarbouriech, Evrard Garcelon, Michal Valko +2
Many popular reinforcement learning problems (e.g., navigation in a maze, some Atari games, mountain car) are instances of the episodic setting under its stochastic shortest path (…
Frequentist Regret Bounds for Randomized Least-Squares Value Iteration
Andrea Zanette, David Brandfonbrener, Emma Brunskill +2
We consider the exploration-exploitation dilemma in finite-horizon reinforcement learning (RL). When the state space is large or continuous, traditional tabular approaches are unfe…
A Structured Prediction Approach for Generalization in Cooperative Multi-Agent Reinforcement Learning
Nicolas Carion, Gabriel Synnaeve, Alessandro Lazaric +1
Effective coordination is crucial to solve multi-agent collaborative (MAC) problems. While centralized reinforcement learning methods can optimally solve small MAC instances, they…
Word-order biases in deep-agent emergent communication
Rahma Chaabouni, Eugene Kharitonov, Alessandro Lazaric +2
Sequence-processing neural networks led to remarkable progress on many NLP tasks. As a consequence, there has been increasing interest in understanding to what extent they process…
Gaussian Process Optimization with Adaptive Sketching: Scalable and No Regret
Daniele Calandriello, Luigi Carratino, Alessandro Lazaric +2
Gaussian processes (GP) are a well studied Bayesian approach for the optimization of black-box functions. Despite their effectiveness in simple problems, GP-based algorithms hardly…
Active Exploration in Markov Decision Processes
Jean Tarbouriech, Alessandro Lazaric
We introduce the active exploration problem in Markov decision processes (MDPs). Each state of the MDP is characterized by a random value and the learner should gather samples to e…