Author name cluster

Carlos E. Luis

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

2 author rows

TMLR Journal 2025 Journal Article

Uncertainty Representations in State-Space Layers for Deep Reinforcement Learning under Partial Observability

Carlos E. Luis
Alessandro Giacomo Bottero
Julia Vinogradska
Felix Berkenkamp
Jan Peters

Optimal decision-making under partial observability requires reasoning about the uncertainty of the environment’s hidden state. However, most reinforcement learning architectures handle partial observability with sequence models that have no internal mechanism to incorporate uncertainty in their hidden state representation, such as recurrent neural networks, deterministic state-space models and transformers. Inspired by advances in probabilistic world models for reinforcement learning, we propose a standalone Kalman filter layer that performs closed-form Gaussian inference in linear state-space models and train it end-to-end within a model-free architecture to maximize returns. Similar to efficient linear recurrent layers, the Kalman filter layer processes sequential data using a parallel scan, which scales logarithmically with the sequence length. By design, Kalman filter layers are a drop-in replacement for other recurrent layers in standard model-free architectures, but importantly they include an explicit mechanism for probabilistic filtering of the latent state representation. Experiments in a wide variety of tasks with partial observability show that Kalman filter layers excel in problems where uncertainty reasoning is key for decision-making, outperforming other stateful models.

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JMLR Journal 2024 Journal Article

Value-Distributional Model-Based Reinforcement Learning

Carlos E. Luis
Alessandro G. Bottero
Julia Vinogradska
Felix Berkenkamp
Jan Peters

Quantifying uncertainty about a policy's long-term performance is important to solve sequential decision-making tasks. We study the problem from a model-based Bayesian reinforcement learning perspective, where the goal is to learn the posterior distribution over value functions induced by parameter (epistemic) uncertainty of the Markov decision process. Previous work restricts the analysis to a few moments of the distribution over values or imposes a particular distribution shape, e.g., Gaussians. Inspired by distributional reinforcement learning, we introduce a Bellman operator whose fixed-point is the value distribution function. Based on our theory, we propose Epistemic Quantile-Regression (EQR), a model-based algorithm that learns a value distribution function. We combine EQR with soft actor-critic (SAC) for policy optimization with an arbitrary differentiable objective function of the learned value distribution. Evaluation across several continuous-control tasks shows performance benefits with respect to both model-based and model-free algorithms. The code is available at https://github.com/boschresearch/dist-mbrl. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2024. ( edit, beta )

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EWRL Workshop 2023 Workshop Paper

Value-Distributional Model-Based Reinforcement Learning

Carlos E. Luis
Alessandro Giacomo Bottero
Julia Vinogradska
Felix Berkenkamp
Jan Peters

Quantifying uncertainty about a policy's long-term performance is key in sequential decision-making tasks. We study the problem from a Bayesian perspective, where the goal is to learn the posterior distribution over value functions induced by parameter (epistemic) uncertainty of the Markov decision process. Previous work restricts the analysis to a few moments of the distribution over values or imposes a particular distribution shape (e. g. , Gaussians). Inspired by distributional reinforcement learning, we introduce a Bellman operator whose fixed-point is the value distribution function. Based on our theory, we propose Epistemic Quantile-Regression (EQR), a model-based algorithm that learns a value distribution function that can be used for policy optimization. Evaluation across several continuous-control tasks shows performance benefits with respect to established model-based and model-free algorithms.

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ICRA Conference 2019 Conference Paper

Fast and In Sync: Periodic Swarm Patterns for Quadrotors

Xintong Du
Carlos E. Luis
Marijan Vukosavljev
Angela P. Schoellig

This paper aims to design quadrotor swarm performances, where the swarm acts as an integrated, coordinated unit embodying moving and deforming objects. We divide the task of creating a choreography into three basic steps: designing swarm motion primitives, transitioning between those movements, and synchronizing the motion of the drones. The result is a flexible framework for designing choreographies comprised of a wide variety of motions. The motion primitives can be intuitively designed using a few parameters, providing a rich library for choreography design. Moreover, we combine and adapt existing goal assignment and trajectory generation algorithms to maximize the smoothness of the transitions between motion primitives. Finally, we propose a correction algorithm to compensate for motion delays and synchronize the motion of the drones to a desired periodic motion pattern. The proposed methodology was validated experimentally by generating and executing choreographies on a swarm of 25 quadrotors.

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