Deep Active Inference as Variational Policy Gradients
This work addresses the problem of scaling Active Inference for broader AI applications, representing an incremental advancement by adapting existing methods to overcome known bottlenecks.
The paper tackled the scalability limitations of Active Inference by proposing a deep learning-based algorithm that approximates key densities with neural networks, enabling application to larger, complex tasks and achieving performance comparable to reinforcement learning baselines on OpenAIGym benchmarks.
Active Inference is a theory of action arising from neuroscience which casts action and planning as a bayesian inference problem to be solved by minimizing a single quantity - the variational free energy. Active Inference promises a unifying account of action and perception coupled with a biologically plausible process theory. Despite these potential advantages, current implementations of Active Inference can only handle small, discrete policy and state-spaces and typically require the environmental dynamics to be known. In this paper we propose a novel deep Active Inference algorithm which approximates key densities using deep neural networks as flexible function approximators, which enables Active Inference to scale to significantly larger and more complex tasks. We demonstrate our approach on a suite of OpenAIGym benchmark tasks and obtain performance comparable with common reinforcement learning baselines. Moreover, our algorithm shows similarities with maximum entropy reinforcement learning and the policy gradients algorithm, which reveals interesting connections between the Active Inference framework and reinforcement learning.