Key: Model-Based Reinforcement Learning, Model Rollouts, Uncertainty Quantification; ExpEnv: Gym MuJoCo; Any-step Dynamics Model Improves Future Predictions for Online and Offline …

Oct 4, 2022 · There are two main approaches to solving RL problems: Policy-based methods: explicitly building a representation of the policy. This is a probabilistic mapping from the states …

Nov 30, 2018 · With a single model, our approach can perform a wide range of tasks, including lifting objects, folding shorts, placing an apple onto a plate, rearranging objects, and covering a …

By applying reinforcement learning techniques, you can create a policy that guides the generation of images toward maximizing specific design criteria—such as diversity, novelty, or adherence …

Sep 28, 2023 · In this work, we introduce a powerful unsupervised pre-training reinforcement learning (RL) framework called VMBRL3, which improves the generalization ability of visual …

PR2L provides a flexible way for shaping representations for reinforcement learning with VLMs. Humans can quickly learn new behaviors by leveraging background world knowledge. In …

Visualizing reinforcement learning (RL) algorithms is crucial for understanding their behavior and performance. Effective visual aids can provide insights into the inner workings of RL models, …

We show how to teach machines to paint like human painters, who can use a small number of strokes to cre-ate fantastic paintings. By employing a neural renderer in model-based Deep …

We present a framework for object-centric perception, prediction, and planning (OP3), a model-based reinforcement learner that predicts and plans over entity variables inferred via an …

Oct 6, 2020 · Plan2Explore learns a world model that predicts future outcomes given past observations o1:t and actions a1:t. To handle high-dimensional image observations, we …