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Randy Lefebvre

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3 papers
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3

AAAI Conference 2025 Conference Paper

On Shallow Planning Under Partial Observability

  • Randy Lefebvre
  • Audrey Durand

Formulating a real-world problem under the Reinforcement Learning framework involves non-trivial design choices, such as selecting a discount factor for the learning objective (dis- counted cumulative rewards), which articulates the planning horizon of the agent. This work investigates the impact of the discount factor on the bias-variance trade-off given structural parameters of the underlying Markov Decision Process. Our results support the idea that a shorter planning horizon might be beneficial, especially under partial observability.

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RLC Conference 2025 Conference Paper

Optimal discounting for offline input-driven MDP

  • Randy Lefebvre
  • Audrey Dur

Offline reinforcement learning has gained a lot of popularity for its potential to solve industry challenges. However, real-world environments 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.

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RLJ Journal 2025 Journal Article

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 environments 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.

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