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Found 6 papers, 2 papers with code
Dyna-LfLH: Learning Agile Navigation in Dynamic Environments from Learned Hallucination
In our new Dynamic Learning from Learned Hallucination (Dyna-LfLH), we design and learn a novel latent distribution and sample dynamic obstacles from it, so the generated training data can be used to learn a motion planner to navigate in dynamic environments.
Building Minimal and Reusable Causal State Abstractions for Reinforcement Learning
Two desiderata of reinforcement learning (RL) algorithms are the ability to learn from relatively little experience and the ability to learn policies that generalize to a range of problem specifications.
Causal Dynamics Learning for Task-Independent State Abstraction
Learning dynamics models accurately is an important goal for Model-Based Reinforcement Learning (MBRL), but most MBRL methods learn a dense dynamics model which is vulnerable to spurious correlations and therefore generalizes poorly to unseen states.
CLAMGen: Closed-Loop Arm Motion Generation via Multi-view Vision-Based RL
We propose a vision-based reinforcement learning (RL) approach for closed-loop trajectory generation in an arm reaching problem.
Particle Smoothing Variational Objectives
We apply SVO to three nonlinear latent dynamics tasks and provide statistics to rigorously quantify the predictions of filtered and smoothed objectives.
Smoothing Nonlinear Variational Objectives with Sequential Monte Carlo
The task of recovering nonlinear dynamics and latent structure from a population recording is a challenging problem in statistical neuroscience motivating the development of novel techniques in time series analysis.
