2024

[Paper Notes] SAM-RL: Sensing-Aware Model-Based Reinforcement Learning via Differentiable Physics-Based Simulation and Rendering - RSS 2023

4 minute read

Published: February 20, 2024

Key information

Model-based reinforcement learning (MBRL) is potentially more sample-efficient than model-free RL.
Integration of differentiable physics-based simulation and rendering facilitates the learning process.
Pipeline: Real2Sim -> Learn@Sim -> Sim2Real to produce efficient policies.

1 minute read

Published: February 06, 2024

This paper learns two models: a world model trained on off-policy sequences through supervised learning, and an actor-critic model to learn behaviors from trajectories predicted by the learned model.
The data collection and learning updates are decoupled, enabling fast training without waiting for the environment. A learner thread continuously trains the world model and actor-critic behavior, while an actor thread in parallel computes actions for environment interaction.