DeepMDP: Learning Continuous Latent Space Models for Representation Learning

Carles Gelada, Saurabh Kumar, Jacob Buckman, Ofir Nachum, Marc G. Bellemare
Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2170-2179, 2019.

Abstract

Many reinforcement learning (RL) tasks provide the agent with high-dimensional observations that can be simplified into low-dimensional continuous states. To formalize this process, we introduce the concept of a \texit{DeepMDP}, a parameterized latent space model that is trained via the minimization of two tractable latent space losses: prediction of rewards and prediction of the distribution over next latent states. We show that the optimization of these objectives guarantees (1) the quality of the embedding function as a representation of the state space and (2) the quality of the DeepMDP as a model of the environment. Our theoretical findings are substantiated by the experimental result that a trained DeepMDP recovers the latent structure underlying high-dimensional observations on a synthetic environment. Finally, we show that learning a DeepMDP as an auxiliary task in the Atari 2600 domain leads to large performance improvements over model-free RL.

Cite this Paper


BibTeX
@InProceedings{pmlr-v97-gelada19a, title = {{D}eep{MDP}: Learning Continuous Latent Space Models for Representation Learning}, author = {Gelada, Carles and Kumar, Saurabh and Buckman, Jacob and Nachum, Ofir and Bellemare, Marc G.}, booktitle = {Proceedings of the 36th International Conference on Machine Learning}, pages = {2170--2179}, year = {2019}, editor = {Chaudhuri, Kamalika and Salakhutdinov, Ruslan}, volume = {97}, series = {Proceedings of Machine Learning Research}, month = {09--15 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v97/gelada19a/gelada19a.pdf}, url = {http://proceedings.mlr.press/v97/gelada19a.html}, abstract = {Many reinforcement learning (RL) tasks provide the agent with high-dimensional observations that can be simplified into low-dimensional continuous states. To formalize this process, we introduce the concept of a \texit{DeepMDP}, a parameterized latent space model that is trained via the minimization of two tractable latent space losses: prediction of rewards and prediction of the distribution over next latent states. We show that the optimization of these objectives guarantees (1) the quality of the embedding function as a representation of the state space and (2) the quality of the DeepMDP as a model of the environment. Our theoretical findings are substantiated by the experimental result that a trained DeepMDP recovers the latent structure underlying high-dimensional observations on a synthetic environment. Finally, we show that learning a DeepMDP as an auxiliary task in the Atari 2600 domain leads to large performance improvements over model-free RL.} }
Endnote
%0 Conference Paper %T DeepMDP: Learning Continuous Latent Space Models for Representation Learning %A Carles Gelada %A Saurabh Kumar %A Jacob Buckman %A Ofir Nachum %A Marc G. Bellemare %B Proceedings of the 36th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2019 %E Kamalika Chaudhuri %E Ruslan Salakhutdinov %F pmlr-v97-gelada19a %I PMLR %P 2170--2179 %U http://proceedings.mlr.press/v97/gelada19a.html %V 97 %X Many reinforcement learning (RL) tasks provide the agent with high-dimensional observations that can be simplified into low-dimensional continuous states. To formalize this process, we introduce the concept of a \texit{DeepMDP}, a parameterized latent space model that is trained via the minimization of two tractable latent space losses: prediction of rewards and prediction of the distribution over next latent states. We show that the optimization of these objectives guarantees (1) the quality of the embedding function as a representation of the state space and (2) the quality of the DeepMDP as a model of the environment. Our theoretical findings are substantiated by the experimental result that a trained DeepMDP recovers the latent structure underlying high-dimensional observations on a synthetic environment. Finally, we show that learning a DeepMDP as an auxiliary task in the Atari 2600 domain leads to large performance improvements over model-free RL.
APA
Gelada, C., Kumar, S., Buckman, J., Nachum, O. & Bellemare, M.G.. (2019). DeepMDP: Learning Continuous Latent Space Models for Representation Learning. Proceedings of the 36th International Conference on Machine Learning, in Proceedings of Machine Learning Research 97:2170-2179 Available from http://proceedings.mlr.press/v97/gelada19a.html.

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