4 papers
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…