Search Results for author: Claas Voelcker

Found 6 papers, 4 papers with code

Temporal-Difference Learning Using Distributed Error Signals

1 code implementation6 Nov 2024 Jonas Guan, Shon Eduard Verch, Claas Voelcker, Ethan C. Jackson, Nicolas Papernot, William A. Cunningham

We design a new deep Q-learning algorithm, Artificial Dopamine, to computationally demonstrate that synchronously distributed, per-layer TD errors may be sufficient to learn surprisingly complex RL tasks.

Q-Learning

When does Self-Prediction help? Understanding Auxiliary Tasks in Reinforcement Learning

1 code implementation25 Jun 2024 Claas Voelcker, Tyler Kastner, Igor Gilitschenski, Amir-Massoud Farahmand

We provide a theoretical analysis of the learning dynamics of observation reconstruction, latent self-prediction, and TD learning in the presence of distractions and observation functions under linear model assumptions.

Auxiliary Learning Prediction +2

Dissecting Deep RL with High Update Ratios: Combatting Value Divergence

no code implementations9 Mar 2024 Marcel Hussing, Claas Voelcker, Igor Gilitschenski, Amir-Massoud Farahmand, Eric Eaton

We show that deep reinforcement learning algorithms can retain their ability to learn without resetting network parameters in settings where the number of gradient updates greatly exceeds the number of environment samples by combatting value function divergence.

Deep Reinforcement Learning

Structured Object-Aware Physics Prediction for Video Modeling and Planning

1 code implementation ICLR 2020 Jannik Kossen, Karl Stelzner, Marcel Hussing, Claas Voelcker, Kristian Kersting

When humans observe a physical system, they can easily locate objects, understand their interactions, and anticipate future behavior, even in settings with complicated and previously unseen interactions.

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