Learning Strategy Fusion

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Learning Strategy Fusion

Learning Strategy Fusion

Learning Strategy (LS) Fusion a method to fuse learning strategies (LSs) in reinforcement learning framework. Generally, we need to choose a suitable LS for each task respectively. In contrast, the proposed method automates this selection by fusing LSs. The LSs fused in this research includes a transfer learning, a hierarchical RL, and a model based RL.

The proposed method has a wide applicability. When the method is applied to a motion learning task, such as a crawling task, the performance of motion may be improved compared to an agent with a single LS. The method also can be applied to a navigation task by hierarchically combining already learned motions, such as a crawling and a turning. Actually, LS fusion was applied to a maze task of a humanoid robot where the robot learns not only a path to goal, but also a crawling and a turning motions.

Learning Strategy Fusion for Multiple Environment

We extended the Learning Strategy Fusion to learn policies across multiple types of environments. The robot quickly adapts to a new environment with preserving policies of past environments. The proposed methods are verified with both a dynamics simulator and real robots.

Learning strategy fusion for multiple environments How the learning strategy fusion works in multiple environments
Fig: Conceptual diagram of the learning strategy fusion (left), and how it works in varying environments (right).

Crawling acquisition through 3 different terrains (learned from scratch):

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