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arXiv · 2011.11293

Evolutionary Planning in Latent Space

Thor V. A. N. Olesen·Dennis T. T. Nguyen·Rasmus Berg Palm·Sebastian Risi
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Abstract

Planning is a powerful approach to reinforcement learning with several desirable properties. However, it requires a model of the world, which is not readily available in many real-life problems. In this paper, we propose to learn a world model that enables Evolutionary Planning in Latent Space (EPLS). We use a Variational Auto Encoder (VAE) to learn a compressed latent representation of individual observations and extend a Mixture Density Recurrent Neural Network (MDRNN) to learn a stochastic, multi-modal forward model of the world that can be used for planning. We use the Random Mutation Hill Climbing (RMHC) to find a sequence of actions that maximize expected reward in this learned model of the world. We demonstrate how to build a model of the world by bootstrapping it with rollouts from a random policy and iteratively refining it with rollouts from an increasingly accurate planning policy using the learned world model. After a few iterations of this refinement, our planning agents are better than standard model-free reinforcement learning approaches demonstrating the viability of our approach.

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Thor V. A. N. Olesen, Dennis T. T. Nguyen, Rasmus Berg Palm, Sebastian Risi. 2020-11-23. Evolutionary Planning in Latent Space. https://arxiv.org/abs/2011.11293

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