Deep Active Inference for Pixel-Based Discrete Control: Evaluation on the Car Racing Problem

• URL: http://arxiv.org/abs/2109.04155v1
• Date: Thu, 9 Sep 2021 10:33:36 GMT
• Title: Deep Active Inference for Pixel-Based Discrete Control: Evaluation on the Car Racing Problem
• Abstract summary: We study the performance of a deep active inference (dAIF) agent on OpenAI's car racing benchmark. State inference and control are learned end-to-end by optimizing the expected free energy. vanilla dAIF does not reach state-of-the-art performance compared to other world model approaches.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite the potential of active inference for visual-based control, learning the model and the preferences (priors) while interacting with the environment is challenging. Here, we study the performance of a deep active inference (dAIF) agent on OpenAI's car racing benchmark, where there is no access to the car's state. The agent learns to encode the world's state from high-dimensional input through unsupervised representation learning. State inference and control are learned end-to-end by optimizing the expected free energy. Results show that our model achieves comparable performance to deep Q-learning. However, vanilla dAIF does not reach state-of-the-art performance compared to other world model approaches. Hence, we discuss the current model implementation's limitations and potential architectures to overcome them.

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