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Found 6 papers, 4 papers with code
Iterated $Q$-Network: Beyond the One-Step Bellman Operator
Value-based Reinforcement Learning (RL) methods rely on the application of the Bellman operator, which needs to be approximated from samples.
Diminishing Return of Value Expansion Methods in Model-Based Reinforcement Learning
Therefore, we conclude that the limitation of model-based value expansion methods is not the model accuracy of the learned models.
Revisiting Model-based Value Expansion
Model-based value expansion methods promise to improve the quality of value function targets and, thereby, the effectiveness of value function learning.
SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving
We open-source the SMARTS platform and the associated benchmark tasks and evaluation metrics to encourage and empower research on multi-agent learning for autonomous driving.
SAMBA: Safe Model-Based & Active Reinforcement Learning
In this paper, we propose SAMBA, a novel framework for safe reinforcement learning that combines aspects from probabilistic modelling, information theory, and statistics.
CrossQ: Batch Normalization in Deep Reinforcement Learning for Greater Sample Efficiency and Simplicity
Sample efficiency is a crucial problem in deep reinforcement learning.
