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Notes on sample efficiency
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Expert Behavior Prior Reinforcement Learning
A new method called Expert Behavior Prior Reinforcement Learning (EBP) is proposed to improve the sample efficiency in online reinforcement learning. This method generates expert policy priors directly from the online…
Knowledge- and Gradient-Guided Reinforcement Learning for Parametrized Action Markov Decision Processes
The paper introduces a new reinforcement learning algorithm called Neuro-Symbolic Knowledge- and Gradient-Guided Reinforcement Learning (KGRL). This algorithm uses domain knowledge to improve the sample efficiency of…
Knowledge- and Gradient-Guided Reinforcement Learning for Parametrized Action Markov Decision Processes
The paper presents a new Reinforcement Learning algorithm named Neuro-Symbolic Knowledge- and Gradient-Guided Reinforcement Learning (KGRL). KGRL uses knowledge from a Datalog knowledge base to prune non-applicable…
Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF
The paper proposes two methods for improving the efficiency of Reinforcement Learning from Human Feedback (RLHF) when applied to diffusion models, which are often inefficient due to high feedback requirements. The…
Automating Potential-based Reward Shaping with Vision Language Model Guidance
Researchers introduce a new framework called VLM-PBRS for automating potential-based reward shaping in reinforcement learning agents. The framework learns the potential function directly from vision language model…
Automating Potential-based Reward Shaping with Vision Language Model Guidance
Researchers have developed a new method called VLM-PBRS for automating potential-based reward shaping in reinforcement learning agents. This method uses vision language models (VLMs) to learn the potential function…
Automating Potential-based Reward Shaping with Vision Language Model Guidance
Researchers have developed a new method for reinforcement learning agents using vision language models (VLMs). The approach called VLM-guided PBRS learns a potential function from VLM feedback to address the challenge…
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