About

Hi, I'm Gaspard Lambrechts, a postdoctoral researcher at the University of Liège. I recently defended my PhD thesis with Prof. Damien Ernst, and previously did an internship with Prof. Aditya Mahajan at McGill University and Mila Québec. My research focuses on reinforcement learning in partially observable environments (RL in POMDP). I am mainly interested in sequence modeling, representation learning, world models, asymmetric learning, and generalization.

I am currently looking for research positions, do not hesitate to contact me.

Publications

• A Theoretical Justification for Asymmetric Actor-Critic Algorithms.
  Lambrechts, Ernst, Mahajan. International Conference on Machine Learning, July 2025.
  Abstract.  Article.  Blog Post.  Slides.

  
  In reinforcement learning for partially observable environments, many successful algorithms were developed under the asymmetric learning paradigm. This paradigm leverages eventual additional state information available at training for faster learning. Although the proposed learning objectives are usually theoretically sound, these methods still lack a theoretical justification for their potential benefits. We propose such a justification for asymmetric actor-critic algorithms with linear function approximators by adapting a finite-time convergence analysis to this setting. The resulting finite-time bound reveals that the asymmetric critic eliminates an error term arising from aliasing in the agent state.
• Learning to Remember the Past by Learning to Remember the Future.
  Lambrechts. University of Liège, April 2025.
  Abstract.  Thesis.  Slides.

  
  Intelligence is usually understood as the ability to make decisions, based on perception, in order to achieve objectives. In other words, intelligence is about perceiving and abstracting past information about the world for then acting on its future execution. This thesis focuses on reinforcement learning in partially observable Markov decision processes for learning intelligent behaviors through interaction. In particular, this manuscript explores and emphasizes the interplay between perception, representations, memory, predictions and decisions. After introducing the theoretical foundations, the core contributions of the thesis are presented across three thematic parts. The first part, “Learning and Remembering,” investigates how learning intelligent behaviors improves memory and vice versa. To begin with, it studies how learning to act optimally results in representations of the perception history that encode the posterior distribution over the states, known as the belief. Next, it studies how long-term memory improves the ability to learn intelligent behaviors, by designing an initialization procedure for recurrent neural networks that endows them with long-term memorization abilities. The second part, “Leveraging Additional Information,” explores how additional information about the world can be used to learn intelligent behaviors faster than when learning from perception only. It starts by empirically showing that world models predicting this additional information provide better history representations and faster learning. Then, it provides a theoretical justification for the improved convergence speed of a particular algorithm that leverages this information, namely the asymmetric actor-critic algorithm. The third part, “Entangling Predictions and Decisions,” proposes several architectural innovations for obtaining world models that efficiently generate trajectories. First, it develops new sequence models that parallelize autoregressive generation, while being implicitly recurrent to allow resuming generation. Afterwards, it elaborates on their use as new world models that are able to generate trajectories in parallel through specific latent policies. Finally, this thesis concludes by summarizing how learning adequate representations of the perception history is paramount to learning to make decisions under partial observability. In the perspective of developing general intelligence, this thesis also motivates the shift from specialized abstractions to generalizable abstractions extending across diverse environments.
• Informed POMDP: Leveraging Additional Information in Model-Based RL.
  Lambrechts, Bolland, Ernst. Reinforcement Learning Conference, August 2024.
  Abstract.  Article.  Poster.  Slides.  Code.

  
  In this work, we generalize the problem of learning through interaction in a POMDP by accounting for eventual additional information available at training time. First, we introduce the informed POMDP, a new learning paradigm offering a clear distinction between the training information and the execution observation. Next, we propose an objective for learning a sufficient statistic from the history for the optimal control that leverages this information. We then show that this informed objective consists of learning an environment model from which we can sample latent trajectories. Finally, we show for the Dreamer algorithm that the convergence speed of the policies is sometimes greatly improved in several environments by using this informed environment model. Those results and the simplicity of the proposed adaptation advocate for a systematic consideration of eventual additional information when learning in a POMDP using model-based RL.
• Parallelizing Autoregressive Generation with Variational State Space Models.
  Lambrechts*, Claes*, Geurts, Ernst. ICML Workshop on Sequence Modeling Architectures, July 2024.
  Abstract.  Article.  Poster.

  
  Attention-based models such as Transformers and recurrent models like state space models (SSMs) have emerged as successful methods for autoregressive sequence modeling. Although both enable parallel training, none enable parallel generation due to their autoregressiveness. We propose the variational SSM (VSSM), a variational autoencoder (VAE) where both the encoder and decoder are SSMs. Since sampling the latent variables and decoding them with the SSM can be parallelized, both training and generation can be conducted in parallel. Moreover, the decoder recurrence allows generation to be resumed without reprocessing the whole sequence. Finally, we propose the autoregressive VSSM that can be conditioned on a partial realization of the sequence, as is common in language generation tasks. Interestingly, the autoregressive VSSM still enables parallel generation. We highlight on toy problems (MNIST, CIFAR) the empirical gains in speed-up and show that it competes with traditional models in terms of generation quality (Transformer, Mamba SSM).
• Warming Up RNNs to Maximize Reachable Multistability Greatly Improves Learning.
  Lambrechts*, De Geeter*, Vecoven*, Ernst, Drion. Neural Networks, August 2023.
  Abstract.  Article.  Blog Post.  Code.

  
  Training recurrent neural networks is known to be difficult when time dependencies become long. In this work, we show that most standard cells only have one stable equilibrium at initialisation, and that learning on tasks with long time dependencies generally occurs once the number of network stable equilibria increases; a property known as multistability. Multistability is often not easily attained by initially monostable networks, making learning of long time dependencies between inputs and outputs difficult. This insight leads to the design of a novel way to initialise any recurrent cell connectivity through a procedure called “warmup” to improve its capability to learn arbitrarily long time dependencies. This initialisation procedure is designed to maximise network reachable multistability, i.e., the number of equilibria within the network that can be reached through relevant input trajectories, in few gradient steps. We show on several information restitution, sequence classification, and reinforcement learning benchmarks that warming up greatly improves learning speed and performance, for multiple recurrent cells, but sometimes impedes precision. We therefore introduce a double-layer architecture initialised with a partial warmup that is shown to greatly improve learning of long time dependencies while maintaining high levels of precision. This approach provides a general framework for improving learning abilities of any recurrent cell when long time dependencies are present. We also show empirically that other initialisation and pretraining procedures from the literature implicitly foster reachable multistability of recurrent cells.
• Recurrent Networks, Hidden States and Beliefs in Partially Observable Environments.
  Lambrechts, Bolland, Ernst. Transaction on Machine Learning Research, August 2022.
  Abstract.  Article.  Blog Post.  Code.

  
  Reinforcement learning aims to learn optimal policies from interaction with environments whose dynamics are unknown. Many methods rely on the approximation of a value function to derive near-optimal policies. In partially observable environments, these functions depend on the complete sequence of observations and past actions, called the history. In this work, we show empirically that recurrent neural networks trained to approximate such value functions internally filter the posterior probability distribution of the current state given the history, called the belief. More precisely, we show that, as a recurrent neural network learns the Q-function, its hidden states become more and more correlated with the beliefs of state variables that are relevant to optimal control. This correlation is measured through their mutual information. In addition, we show that the expected return of an agent increases with the ability of its recurrent architecture to reach a high mutual information between its hidden states and the beliefs. Finally, we show that the mutual information between the hidden states and the beliefs of variables that are irrelevant for optimal control decreases through the learning process. In summary, this work shows that in its hidden states, a recurrent neural network approximating the Q-function of a partially observable environment reproduces a sufficient statistic from the history that is correlated to the relevant part of the belief for taking optimal actions.

Preprints

• Off-Policy Maximum Entropy RL with Future State and Action Visitation Measures.
  Bolland, Lambrechts, Ernst. Preprint, March 2025.
  Abstract.  Preprint.

  
  We introduce a new maximum entropy reinforcement learning framework based on the distribution of states and actions visited by a policy. More precisely, an intrinsic reward function is added to the reward function of the Markov decision process that shall be controlled. For each state and action, this intrinsic reward is the relative entropy of the discounted distribution of states and actions (or features from these states and actions) visited during the next time steps. We first prove that an optimal exploration policy, which maximizes the expected discounted sum of intrinsic rewards, is also a policy that maximizes a lower bound on the state-action value function of the decision process under some assumptions. We also prove that the visitation distribution used in the intrinsic reward definition is the fixed point of a contraction operator. Following, we describe how to adapt existing algorithms to learn this fixed point and compute the intrinsic rewards to enhance exploration. A new practical off-policy maximum entropy reinforcement learning algorithm is finally introduced. Empirically, exploration policies have good state-action space coverage, and high-performing control policies are computed efficiently.
• Reinforcement Learning to Improve Delta Robot Throws for Sorting Scrap Metal.
  Louette, Lambrechts, Ernst, Pirard, Dislaire. Preprint, June 2024.
  Abstract.  Preprint.

  
  This study proposes a novel approach based on reinforcement learning (RL) to enhance the sorting efficiency of scrap metal using delta robots and a Pick-and-Place (PaP) process, widely used in the industry. We use three classical model-free RL algorithms (TD3, SAC and PPO) to reduce the time to sort metal scraps. We learn the release position and speed needed to throw an object in a bin instead of moving to the exact bin location, as with the classical PaP technique. Our contribution is threefold. First, we provide a new simulation environment for learning RL-based Pick-and-Throw (PaT) strategies for parallel grippers. Second, we use RL algorithms for learning this task in this environment resulting in 89.32% accuracy while speeding up the throughput by 51% in simulation. Third, we evaluate the performances of RL algorithms and compare them to a PaP and a state-of-the-art PaT method both in simulation and reality, learning only from simulation with domain randomisation and without fine tuning in reality to transfer our policies. This work shows the benefits of RL-based PaT compared to PaP or classical optimization PaT techniques used in the industry. The code is available at https://github.com/louettearthur/pick-and-throw.
• Behind the Myth of Exploration in Policy Gradients.
  Bolland, Lambrechts, Ernst. Preprint, May 2024.
  Abstract.  Preprint.

  
  Policy-gradient algorithms are effective reinforcement learning methods for solving control problems with continuous state and action spaces. To compute near-optimal policies, it is essential in practice to include exploration terms in the learning objective. Although the effectiveness of these terms is usually justified by an intrinsic need to explore environments, we propose a novel analysis and distinguish two different implications of these techniques. First, they make it possible to smooth the learning objective and to eliminate local optima while preserving the global maximum. Second, they modify the gradient estimates, increasing the probability that the stochastic parameter update eventually provides an optimal policy. In light of these effects, we discuss and illustrate empirically exploration strategies based on entropy bonuses, highlighting their limitations and opening avenues for future works in the design and analysis of such strategies.

Talks

• Partial Observability and Asymmetric Observability.
  BeNeRL Workshop, July 4th, 2025.  Slides.
• A Theoretical Justification for Asymmetric Actor-Critic Algorithms.
  Mila RL Sofa, December 20th, 2024.  Slides.
• Informed POMDP: Leveraging Additional Information in Model-Based RL.
  Reinforcement Learning Conference, August 12th, 2024.  Slides.
• Learning to Remember the Past by Learning to Predict the Future.
  VUB Reinforcement Learning Talks, November 17th, 2023.  Slides.

Teaching

• [DATS0001] Foundations of Data Science
• [INFO8003] Optimal Decision Making

Code

Contact

 

Gaspard Lambrechts, I.106
Montefiore Institute, B28
Allée de la découverte, 10
4000 Liège
Belgium