1/5/2024 0 Comments A deeper look at step 4![]() ![]() ![]() ![]() We demonstrate that REM-Dyna exhibits similar advantages over replay-based methods in learning in continuous state problems, and that the performance gap grows when moving to stochastic domains, of increasing size. We introduce a semi-parametric model learning approach, called Reweighted Experience Models (REMs), that makes it simple to sample next states or predecessors. Perspective taking allows for the growth and further application of our own knowledge by expanding our own perspective. We show that a model, as opposed to a replay buffer, is particularly useful for specifying which states to sample from during planning, such as predecessor states that propagate information in reverse from a state more quickly. Replay-based methods can be seen as stochastic planning methods that repeatedly sample from a buffer of recent agent-environment interactions and perform updates to improve data efficiency. We first highlight the flexibility afforded by a model over Experience Replay (ER). The aim of this paper is to revisit sample-based planning, in stochastic and continuous domains with learned models. This elegant planning strategy has been mostly explored in the tabular setting. Dyna is a planning paradigm that naturally interleaves learning and planning, by simulating one-step experience to update the action-value function. introduce the Natural Scenes Dataset high-resolution fMRI data from eight individuals scanned as they collectively viewed more than 70,000. Download a PDF of the paper titled Organizing Experience: A Deeper Look at Replay Mechanisms for Sample-based Planning in Continuous State Domains, by Yangchen Pan and 4 other authors Download PDF Abstract:Model-based strategies for control are critical to obtain sample efficient learning. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |