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Notes on policy learning
7 notes
On-Policy and Off-Policy Learning for Large Action Spaces
The paper presents research on policy learning in interactive systems with large action spaces. The study focuses on two paradigms: on-policy and off-policy learning, both of which face challenges like inefficient…
On-Policy and Off-Policy Learning for Large Action Spaces
The paper by Imad Aouali discusses policy learning in interactive systems with large action spaces, where an agent makes decisions and receives partial feedback based on a context. The work focuses on two paradigms…
On-Policy and Off-Policy Learning for Large Action Spaces
The paper discusses policy learning in interactive systems with large action spaces, where agents observe contexts, select actions, and receive partial feedback. The research focuses on two paradigms: on-policy…
DLAM: Distributional Latent Actions with Temporal Constraints
The paper introduces DLAM, a distributional latent-action model that aims to overcome the limitations of existing models by representing each transition as a diagonal Gaussian. This model is designed to learn more…
Latent Actions from Factorized Transition Effects under Agent Ambiguity
The research introduces Observed Transition Factorization (OTF), a method to decompose transitions in multi-object or distracting scenes into reusable transition primitives. These primitives are used to form…
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…
Joint Learning of Experiential Rules and Policies for Large Language Model Agents
Researchers present a method called Joint Learning of Experiential Rules and Policies for Large Language Model Agents (JERP). This method aims to help LLM agents, which operate in multi-step interactive environments…
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