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Peter Böhm

2 accepted papers

2023

Feature Extraction for Effective and Efficient Deep Reinforcement Learning on Real Robotic Platforms

ICRA 2023poster

Deep reinforcement learning (DRL) methods can solve complex continuous control tasks in simulated environments by taking actions based solely on state observations at each decision point. Because of the dynamics involved, individual snapshots of real-world sensor measurements afford only partial sta…

Cited by 4SourceScholar
2022

Non-blocking Asynchronous Training for Reinforcement Learning in Real-World Environments

IROS 2022poster

Deep Reinforcement Learning (DRL) faces challenges bridging the sim-to-real gap to enable real-world applications. In contrast to the simulated environments used in conventional DRL training, real-world systems are non-linear and evolve in an asynchronous fashion; sensors and actuators have limited…

Cited by 8SourceScholar