1 citations · 1 across the 8 of their papers we have counts for
8 papers
The Dually Flat Geometry of Planning as Inference
Nikola Milosevic, Asaki Kataoka, Nicolas Hinrichs +2
We present an alternative characterization of the occupancy measure of reinforcement learning, obtained by embedding the planning criterion into the dynamics through a resetting pl…
Active Inference as a Convex Markov Decision Process
Nikola Milosevic, Nicolás Hinrichs, Nico Scherf
Active Inference (AIF) frames adaptive behavior as the minimization of expected free energy (EFE), combining epistemic and pragmatic objectives within a single variational principl…
Stochastic Decision Horizons for Survival-Based Constrained Reinforcement Learning
Nikola Milosevic, Leonard Franz, Daniel Haeufle +3
We propose stochastic decision horizons (SDH), a theoretically grounded framework for survival-based constrained reinforcement learning from per-step violation signals. Rather than…
The Geometry of Nonlinear Reinforcement Learning
Nikola Milosevic, Nico Scherf
Reward maximization, safe exploration, and intrinsic motivation are often studied as separate objectives in reinforcement learning (RL). We present a unified geometric framework, t…
Physical Embodiment Enables Information Processing Beyond Explicit Sensing in Active Matter
Diptabrata Paul, Nikola Milosevic, Nico Scherf +1
Living microorganisms have evolved dedicated sensory machinery to detect environmental perturbations, processing these signals through biochemical networks to guide behavior. Repli…
Central Path Proximal Policy Optimization
Nikola Milosevic, Johannes Müller, Nico Scherf
In constrained Markov decision processes, enforcing constraints during training is often thought of as decreasing the final return. Recently, it was shown that constraints can be i…
Embedding Safety into RL: A New Take on Trust Region Methods
Nikola Milosevic, Johannes Müller, Nico Scherf
Reinforcement Learning (RL) agents can solve diverse tasks but often exhibit unsafe behavior. Constrained Markov Decision Processes (CMDPs) address this by enforcing safety constra…
Open Problem: Active Representation Learning
Nikola Milosevic, Gesine Müller, Jan Huisken +1
In this work, we introduce the concept of Active Representation Learning, a novel class of problems that intertwines exploration and representation learning within partially observ…