Automatic tuning of hyper-parameters of reinforcement learning algorithms using Bayesian optimization with behavioral cloning

• URL: http://arxiv.org/abs/2112.08094v1
• Date: Wed, 15 Dec 2021 13:10:44 GMT
• Title: Automatic tuning of hyper-parameters of reinforcement learning algorithms using Bayesian optimization with behavioral cloning
• Authors: Juan Cruz Barsce, Jorge A. Palombarini, Ernesto C. Mart\'inez
• Abstract summary: In reinforcement learning (RL), the information content of data gathered by the learning agent is dependent on the setting of many hyper- parameters. In this work, a novel approach for autonomous hyper- parameter setting using Bayesian optimization is proposed. Experiments reveal promising results compared to other manual tweaking and optimization-based approaches.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Optimal setting of several hyper-parameters in machine learning algorithms is key to make the most of available data. To this aim, several methods such as evolutionary strategies, random search, Bayesian optimization and heuristic rules of thumb have been proposed. In reinforcement learning (RL), the information content of data gathered by the learning agent while interacting with its environment is heavily dependent on the setting of many hyper-parameters. Therefore, the user of an RL algorithm has to rely on search-based optimization methods, such as grid search or the Nelder-Mead simplex algorithm, that are very inefficient for most RL tasks, slows down significantly the learning curve and leaves to the user the burden of purposefully biasing data gathering. In this work, in order to make an RL algorithm more user-independent, a novel approach for autonomous hyper-parameter setting using Bayesian optimization is proposed. Data from past episodes and different hyper-parameter values are used at a meta-learning level by performing behavioral cloning which helps improving the effectiveness in maximizing a reinforcement learning variant of an acquisition function. Also, by tightly integrating Bayesian optimization in a reinforcement learning agent design, the number of state transitions needed to converge to the optimal policy for a given task is reduced. Computational experiments reveal promising results compared to other manual tweaking and optimization-based approaches which highlights the benefits of changing the algorithm hyper-parameters to increase the information content of generated data.

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