Guided Policy Search for Sequential Multitask Learning
نویسندگان
چکیده
منابع مشابه
Learning Policies for Data Imputation with Guided Policy Search
We explore the relationship between directed generative models and reinforcement learning by developing a new approach to data imputation that combines ideas from both areas. We address data imputation by defining an MDP for which we construct policies parametrized by (reasonably) large neural networks. We then show how to train these policies using a form of (self) Guided Policy Search (Levine...
متن کاملScalable Multitask Policy Gradient Reinforcement Learning
Policy search reinforcement learning (RL) allows agents to learn autonomously with limited feedback. However, such methods typically require extensive experience for successful behavior due to their tabula rasa nature. Multitask RL is an approach, which aims to reduce data requirements by allowing knowledge transfer between tasks. Although successful, current multitask learning methods suffer f...
متن کاملModular Multitask Reinforcement Learning with Policy Sketches
We describe a framework for multitask deep reinforcement learning guided by policy sketches. Sketches annotate each task with a sequence of named subtasks, providing high-level structural relationships among tasks, but not providing the detailed guidance required by previous work on learning policy abstractions for RL (e.g. intermediate rewards, subtask completion signals, or intrinsic motivati...
متن کاملGuided Policy Search
Direct policy search can effectively scale to high-dimensional systems, but complex policies with hundreds of parameters often present a challenge for such methods, requiring numerous samples and often falling into poor local optima. We present a guided policy search algorithm that uses trajectory optimization to direct policy learning and avoid poor local optima. We show how differential dynam...
متن کاملPolicy Search for Imitation Learning
Efficient motion planning and possibilities for non-experts to teach new motion primitives are key components for a new generation of robotic systems. In order to be applicable beyond the well-defined context of laboratories and the fixed settings of industrial factories, those machines have to be easily programmable, adapt to dynamic environments and learn and acquire new skills autonomously. ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Systems, Man, and Cybernetics: Systems
سال: 2019
ISSN: 2168-2216,2168-2232
DOI: 10.1109/tsmc.2018.2800040