Tootfinder

This https://arxiv.org/abs/2505.00546 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Directly Forecasting Belief for Reinforcement Learning with Delays
Reinforcement learning (RL) with delays is challenging as sensory perceptions lag behind the actual events: the RL agent needs to estimate the real state of its environment based on past observations. State-of-the-art (SOTA) methods typically employ recursive, step-by-step forecasting of states. This can cause the accumulation of compounding errors. To tackle this problem, our novel belief estimation method, named Directly Forecasting Belief Transformer (DFBT), directly forecasts states from ob…

Data-assimilated model-informed reinforcement learning
Defne E. Ozan, Andrea N\'ovoa, Georgios Rigas, Luca Magri
https://arxiv.org/abs/2506.01755 https…

Data-assimilated model-informed reinforcement learning
The control of spatio-temporally chaos is challenging because of high dimensionality and unpredictability. Model-free reinforcement learning (RL) discovers optimal control policies by interacting with the system, typically requiring observations of the full physical state.In practice, sensors often provide only partial and noisy measurements (observations) of the system. The objective of this paper is to develop a framework that enables the control of chaotic systems with partial and noisy obse…

Cognitive Effort in the Two-Step Task: An Active Inference Drift-Diffusion Model Approach
Alvaro Garrido Perez, Viktor Lemoine, Amrapali Pednekar, Yara Khaluf, Pieter Simoens
https://arxiv.org/abs/2508.04435

Cognitive Effort in the Two-Step Task: An Active Inference Drift-Diffusion Model Approach
High-level theories rooted in the Bayesian Brain Hypothesis often frame cognitive effort as the cost of resolving the conflict between habits and optimal policies. In parallel, evidence accumulator models (EAMs) provide a mechanistic account of how effort arises from competition between the subjective values of available options. Although EAMs have been combined with frameworks like Reinforcement Learning to bridge the gap between high-level theories and process-level mechanisms, relatively les…

This https://arxiv.org/abs/2506.01755 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Data-assimilated model-informed reinforcement learning
The control of spatio-temporally chaos is challenging because of high dimensionality and unpredictability. Model-free reinforcement learning (RL) discovers optimal control policies by interacting with the system, typically requiring observations of the full physical state.In practice, sensors often provide only partial and noisy measurements (observations) of the system. The objective of this paper is to develop a framework that enables the control of chaotic systems with partial and noisy obse…