Amortized Variational Deep Q Network
This work addresses the problem of exploration-exploitation balance for reinforcement learning practitioners, offering an incremental improvement with reduced parameters and training time.
The paper tackles efficient exploration in deep reinforcement learning by proposing an amortized variational inference framework to approximate the posterior distribution of the action value function in Deep Q Networks, resulting in significantly better performance and much less training time than state-of-the-art methods on classical control and chain Markov Decision Process tasks.
Efficient exploration is one of the most important issues in deep reinforcement learning. To address this issue, recent methods consider the value function parameters as random variables, and resort variational inference to approximate the posterior of the parameters. In this paper, we propose an amortized variational inference framework to approximate the posterior distribution of the action value function in Deep Q Network. We establish the equivalence between the loss of the new model and the amortized variational inference loss. We realize the balance of exploration and exploitation by assuming the posterior as Cauchy and Gaussian, respectively in a two-stage training process. We show that the amortized framework can results in significant less learning parameters than existing state-of-the-art method. Experimental results on classical control tasks in OpenAI Gym and chain Markov Decision Process tasks show that the proposed method performs significantly better than state-of-art methods and requires much less training time.