Portrait de Randy Lefebvre

Randy Lefebvre

Doctorat - Université Laval
Superviseur⋅e principal⋅e
Audrey Durand
Sujets de recherche
Apprentissage par renforcement

Publications

Optimal discounting for offline input-driven MDP
Randy Lefebvre
Audrey Durand
Offline reinforcement learning has gained a lot of popularity for its potential to solve industry challenges. However, real-world environmen… (voir plus)ts are often highly stochastic and partially observable, leading long-term planners to overfit to offline data in model-based settings. Input-driven Markov Decision Processes (IDMDPs) offer a way to work with some of the uncertainty by letting designers separate what the agent has control over (states) from what it cannot (inputs) in the environnement. These stochastic external inputs are often difficult to model. Under the assumption that the input model will be imperfect, we investigate the bias-variance tradeoff under shallow planning in IDMDPs. Paving the way to input-driven planning horizons, we also investigate the similarity of optimal planning horizons at different inputs given the structure of the input space.
2025-05-09
rl-conference.cc/RLC/2025/Conference (accepté)
openreview.net
openreview.net
Optimal discounting for offline input-driven MDP
Randy Lefebvre
Audrey Durand
Offline reinforcement learning has gained a lot of popularity for its potential to solve industry challenges. However, real-world environmen… (voir plus)ts are often highly stochastic and partially observable, leading long-term planners to overfit to offline data in model-based settings. Input-driven Markov Decision Processes (IDMDPs) offer a way to work with some of the uncertainty by letting designers separate what the agent has control over (states) from what it cannot (inputs) in the environnement. These stochastic external inputs are often difficult to model. Under the assumption that the input model will be imperfect, we investigate the bias-variance tradeoff under shallow planning in IDMDPs. Paving the way to input-driven planning horizons, we also investigate the similarity of optimal planning horizons at different inputs given the structure of the input space.
2025-05-09
rl-conference.cc/RLC/2025/Conference (publié)
openreview.net
openreview.net
On shallow planning under partial observability
Randy Lefebvre
Audrey Durand
2024-05-31
rl-conference.cc/RLC/2024/Workshop/Deployable_RL (publié)
openreview.net
openreview.net