Model Predictive Control with Self-supervised Representation Learning

Over the last few years, we have not seen any major developments in model-free or model-based learning methods that would make one obsolete relative to the other. In most cases, the used technique is heavily dependent on the use case scenario or other attributes, e.g. the environment. Both approaches have their own advantages, for example, sample efficiency or computational efficiency. However, when combining the two, the advantages of each can be combined and hence achieve better performance. The TD-MPC framework is an example of this approach. On the one hand, a world model in combination with model predictive control is used to get a good initial estimate of the value function. On the other hand, a Q function is used to provide a good long-term estimate. Similar to algorithms like MuZero a latent state representation is used, where only task-relevant information is encoded to reduce the complexity. In this paper, we propose the use of a reconstruction function within the TD-MPC framework, so that the agent can reconstruct the original observation given the internal state representation. This allows our agent to have a more stable learning signal during training and also improves sample efficiency. Our proposed addition of another loss term leads to improved performance on both state- and image-based tasks from the DeepMind-Control suite.

@Article{MHKW23, 
 	 author =  {Matthies, Jonas and Hafez, Burhan and Kotb, Mostafa and Wermter, Stefan},  
 	 title = {Model Predictive Control with Self-supervised Representation Learning}, 
 	 booktitle = {},
 	 journal = {arXiv preprint arXiv:2304.07219},
 	 number = {},
 	 volume = {},
 	 pages = {},
 	 year = {2023},
 	 month = {Apr},
 	 publisher = {},
 	 doi = {}, 
 }