Related papers: Plug and Play, Model-Based Reinforcement Learning

Plug and Play, Model-Based Reinforcement Learning

URL: http://arxiv.org/abs/2108.08960v1
Date: Fri, 20 Aug 2021 01:20:15 GMT
Title: Plug and Play, Model-Based Reinforcement Learning
Authors: Majid Abdolshah, Hung Le, Thommen Karimpanal George, Sunil Gupta, Santu Rana, Svetha Venkatesh
Abstract summary: We introduce an object-based representation that allows zero-shot integration of new objects from known object classes. This is achieved by representing the global transition dynamics as a union of local transition functions. Experiments show that our representation can achieve sample-efficiency in a variety of set-ups.
Score: 60.813074750879615
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Sample-efficient generalisation of reinforcement learning approaches have always been a challenge, especially, for complex scenes with many components. In this work, we introduce Plug and Play Markov Decision Processes, an object-based representation that allows zero-shot integration of new objects from known object classes. This is achieved by representing the global transition dynamics as a union of local transition functions, each with respect to one active object in the scene. Transition dynamics from an object class can be pre-learnt and thus would be ready to use in a new environment. Each active object is also endowed with its reward function. Since there is no central reward function, addition or removal of objects can be handled efficiently by only updating the reward functions of objects involved. A new transfer learning mechanism is also proposed to adapt reward function in such cases. Experiments show that our representation can achieve sample-efficiency in a variety of set-ups.

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