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Found 9 papers, 6 papers with code
Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization
In this work, we explore CrossQ's scaling behavior with higher UTD ratios.
Diminishing Return of Value Expansion Methods
Model-based reinforcement learning aims to increase sample efficiency, but the accuracy of dynamics models and the resulting compounding errors are often seen as key limitations.
Model-based Reinforcement Learning
reinforcement-learning
+1
ROS-LLM: A ROS framework for embodied AI with task feedback and structured reasoning
Key features of the framework include: integration of ROS with an AI agent connected to a plethora of open-source and commercial LLMs, automatic extraction of a behavior from the LLM output and execution of ROS actions/services, support for three behavior modes (sequence, behavior tree, state machine), imitation learning for adding new robot actions to the library of possible actions, and LLM reflection via human and environment feedback.
Iterated $Q$-Network: Beyond One-Step Bellman Updates in Deep Reinforcement Learning
It has been observed that this scheme can be potentially generalized to carry out multiple iterations of the Bellman operator at once, benefiting the underlying learning algorithm.
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.
