Romain Laroche

research

∙ 06/22/2023

Harnessing Mixed Offline Reinforcement Learning Datasets via Trajectory Weighting

Most offline reinforcement learning (RL) algorithms return a target poli...

0 Zhang-Wei Hong, et al. ∙

research

∙ 11/02/2022

Behavior Prior Representation learning for Offline Reinforcement Learning

Offline reinforcement learning (RL) struggles in environments with rich ...

0 Hongyu Zang, et al. ∙

research

∙ 11/01/2022

Discrete Factorial Representations as an Abstraction for Goal Conditioned Reinforcement Learning

Goal-conditioned reinforcement learning (RL) is a promising direction fo...

0 Riashat Islam, et al. ∙

research

∙ 06/02/2022

Expressiveness and Learnability: A Unifying View for Evaluating Self-Supervised Learning

We propose a unifying view to analyze the representation quality of self...

0 Yuchen Lu, et al. ∙

research

∙ 06/02/2022

Incorporating Explicit Uncertainty Estimates into Deep Offline Reinforcement Learning

Most theoretically motivated work in the offline reinforcement learning ...

0 David Brandfonbrener, et al. ∙

research

∙ 06/02/2022

When does return-conditioned supervised learning work for offline reinforcement learning?

Several recent works have proposed a class of algorithms for the offline...

0 David Brandfonbrener, et al. ∙

research

∙ 05/27/2022

Non-Markovian policies occupancy measures

A central object of study in Reinforcement Learning (RL) is the Markovia...

0 Romain Laroche, et al. ∙

research

∙ 03/09/2022

One-Shot Learning from a Demonstration with Hierarchical Latent Language

Humans have the capability, aided by the expressive compositionality of ...

3 Nathaniel Weir, et al. ∙

research

∙ 02/15/2022

Beyond the Policy Gradient Theorem for Efficient Policy Updates in Actor-Critic Algorithms

In Reinforcement Learning, the optimal action at a given state is depend...

0 Romain Laroche, et al. ∙

research

∙ 02/14/2022

On the Chattering of SARSA with Linear Function Approximation

SARSA, a classical on-policy control algorithm for reinforcement learnin...

0 Shangtong Zhang, et al. ∙

research

∙ 11/04/2021

Global Optimality and Finite Sample Analysis of Softmax Off-Policy Actor Critic under State Distribution Mismatch

In this paper, we establish the global optimality and convergence rate o...

0 Shangtong Zhang, et al. ∙

research

∙ 09/29/2021

Batched Bandits with Crowd Externalities

In Batched Multi-Armed Bandits (BMAB), the policy is not allowed to be u...

0 Romain Laroche, et al. ∙

research

∙ 09/29/2021

Dr Jekyll and Mr Hyde: the Strange Case of Off-Policy Policy Updates

The policy gradient theorem states that the policy should only be update...

0 Romain Laroche, et al. ∙

research

∙ 09/13/2021

The Emergence of the Shape Bias Results from Communicative Efficiency

By the age of two, children tend to assume that new word categories are ...

7 Eva Portelance, et al. ∙

research

∙ 05/31/2021

Multi-Objective SPIBB: Seldonian Offline Policy Improvement with Safety Constraints in Finite MDPs

We study the problem of Safe Policy Improvement (SPI) under constraints ...

5 Harsh Satija, et al. ∙

research

∙ 10/02/2020

A Deeper Look at Discounting Mismatch in Actor-Critic Algorithms

We investigate the discounting mismatch in actor-critic algorithm implem...

1 Shangtong Zhang, et al. ∙

research

∙ 02/22/2020

Reinforcement Learning Framework for Deep Brain Stimulation Study

Malfunctioning neurons in the brain sometimes operate synchronously, rep...

0 Dmitrii Krylov, et al. ∙

research

∙ 02/21/2020

Learning Dynamic Knowledge Graphs to Generalize on Text-Based Games

Playing text-based games requires skill in processing natural language a...

0 Ashutosh Adhikari, et al. ∙

research

∙ 10/21/2019

Building Dynamic Knowledge Graphs from Text-based Games

We are interested in learning how to update Knowledge Graphs (KG) from t...

0 Mikuláš Zelinka, et al. ∙

research

∙ 09/11/2019

Safe Policy Improvement with an Estimated Baseline Policy

Previous work has shown the unreliability of existing algorithms in the ...

0 Thiago D. Simão, et al. ∙

research

∙ 07/11/2019

Safe Policy Improvement with Soft Baseline Bootstrapping

Batch Reinforcement Learning (Batch RL) consists in training a policy us...

0 Kimia Nadjahi, et al. ∙

research

∙ 03/03/2019

Scaling up budgeted reinforcement learning

Can we learn a control policy able to adapt its behaviour in real time s...

0 Nicolas Carrara, et al. ∙

research

∙ 11/19/2018

Decentralized Exploration in Multi-Armed Bandits

We consider the decentralized exploration problem: a set of players coll...

0 Raphael Feraud, et al. ∙

research

∙ 06/29/2018

Counting to Explore and Generalize in Text-based Games

We propose a recurrent RL agent with an episodic exploration mechanism t...

0 Xingdi Yuan, et al. ∙

research

∙ 12/19/2017

Safe Policy Improvement with Baseline Bootstrapping

A common goal in Reinforcement Learning is to derive a good strategy giv...

0 Romain Laroche, et al. ∙

research

∙ 07/05/2017

The Complex Negotiation Dialogue Game

This position paper formalises an abstract model for complex negotiation...

0 Romain Laroche, et al. ∙

research

∙ 04/03/2017

Multi-Advisor Reinforcement Learning

We consider tackling a single-agent RL problem by distributing it to n l...

0 Romain Laroche, et al. ∙

research

∙ 01/30/2017

Reinforcement Learning Algorithm Selection

This paper formalises the problem of online algorithm selection in the c...

0 Romain Laroche, et al. ∙

research

∙ 12/15/2016

Separation of Concerns in Reinforcement Learning

In this paper, we propose a framework for solving a single-agent task by...

0 Harm van Seijen, et al. ∙

Romain Laroche

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