12 citations · 33 across the 10 of their papers we have counts for
10 papers
PASTA: Pretrained Action-State Transformer Agents
Raphael Boige, Yannis Flet-Berliac, Arthur Flajolet +2
Self-supervised learning has brought about a revolutionary paradigm shift in various computing domains, including NLP, vision, and biology. Recent approaches involve pre-training t…
Waypoint Transformer: Reinforcement Learning via Supervised Learning with Intermediate Targets
Anirudhan Badrinath, Yannis Flet-Berliac, Allen Nie +1
Despite the recent advancements in offline reinforcement learning via supervised learning (RvS) and the success of the decision transformer (DT) architecture in various domains, DT…
Model-based Offline Reinforcement Learning with Local Misspecification
Kefan Dong, Yannis Flet-Berliac, Allen Nie +1
We present a model-based offline reinforcement learning policy performance lower bound that explicitly captures dynamics model misspecification and distribution mismatch and we pro…
Data-Efficient Pipeline for Offline Reinforcement Learning with Limited Data
Allen Nie, Yannis Flet-Berliac, Deon R. Jordan +2
Offline reinforcement learning (RL) can be used to improve future performance by leveraging historical data. There exist many different algorithms for offline RL, and it is well re…
Offline Policy Optimization with Eligible Actions
Yao Liu, Yannis Flet-Berliac, Emma Brunskill
Offline policy optimization could have a large impact on many real-world decision-making problems, as online learning may be infeasible in many applications. Importance sampling an…
SAAC: Safe Reinforcement Learning as an Adversarial Game of Actor-Critics
Yannis Flet-Berliac, Debabrota Basu
Although Reinforcement Learning (RL) is effective for sequential decision-making problems under uncertainty, it still fails to thrive in real-world systems where risk or safety is…
Adversarially Guided Actor-Critic
Yannis Flet-Berliac, Johan Ferret, Olivier Pietquin +2
Despite definite success in deep reinforcement learning problems, actor-critic algorithms are still confronted with sample inefficiency in complex environments, particularly in tas…
Learning Value Functions in Deep Policy Gradients using Residual Variance
Yannis Flet-Berliac, Reda Ouhamma, Odalric-Ambrym Maillard +1
Policy gradient algorithms have proven to be successful in diverse decision making and control tasks. However, these methods suffer from high sample complexity and instability issu…
MERL: Multi-Head Reinforcement Learning
Yannis Flet-Berliac, Philippe Preux
A common challenge in reinforcement learning is how to convert the agent's interactions with an environment into fast and robust learning. For instance, earlier work makes use of d…
Only Relevant Information Matters: Filtering Out Noisy Samples to Boost RL
Yannis Flet-Berliac, Philippe Preux
In reinforcement learning, policy gradient algorithms optimize the policy directly and rely on sampling efficiently an environment. Nevertheless, while most sampling procedures are…