]> 2026-04-04T10:01:43+02:00 Adaptive Knowledge Aggregation in Asynchronous Reinforcement Learning 0 en Master Develop an adaptive method to combine knowledge from multiple reinforcement learning agents more efficiently in asynchronous setups. Deep Reinforcement Learning course. Good knowledge of Machine Learning and preferably Reinforcement learning. Good programming skills - Required for the implementation of investigated methods. Mnih, V. et al., Asynchronous Methods for Deep Reinforcement Learning. (2016) Shen, H. et al., Towards Understanding Asynchronous Advantage Actor–Critic: Convergence and Linear Speedup. (2020) Ma, J. et al., FedStaleWeight: Buffered Asynchronous Federated Learning with Fair Aggregation via Staleness Reweighting. (2024) Wu, Y. et al., Uncertainty Weighted Actor–Critic for Offline Reinforcement Learning. (2021) Kumar, A. et al., Adaptive aggregation for RL in average reward MDPs. (2012) Littlestone, N. & Warmuth, M., The Weighted Majority Algorithm. (1994) Open Fall 2025 Adaptive Knowledge Aggregation in Asynchronous Reinforcement Learning 2025-09-08T13:26:16Z 2460927.0599074 Adaptive Knowledge Aggregation in Asynchronous Reinforcement Learning 0 [2,"smw-datavalue-property-restricted-annotation-use","Keywords"] Adaptive Knowledge Aggregation in Asynchronous Reinforcement Learning# ERRd9c33d5ac8a89308063e13c4b212fa7c