AgentForge: A Flexible Low-Code Platform for Reinforcement Learning Agent Design

Developing a reinforcement learning (RL) agent often involves identifying effective values for a large number of parameters, covering the policy, reward function, environment, and the agent's internal architecture, such as parameters controlling how the peripheral vision and memory modules work. Critically, since these parameters are interrelated in complex ways, optimizing them can be viewed as a black box optimization problem, which is especially challenging for non-experts. Although existing optimization-as-a-service platforms (e.g., Vizier, Optuna) can handle such problems, they are impractical for RL systems, as users must manually map each parameter to different components, making the process cumbersome and error-prone. They also require deep understanding of the optimization process, limiting their application outside ML experts and restricting access for fields like cognitive science, which models human decision-making. To tackle these challenges, we present AgentForge, a flexible low-code framework to optimize any parameter set across an RL system. AgentForge allows the user to perform individual or joint optimization of parameter sets. An optimization problem can be defined in a few lines of code and handed to any of the interfaced optimizers. We evaluated its performance in a challenging vision-based RL problem. AgentForge enables practitioners to develop RL agents without requiring extensive coding or deep expertise in optimization.

Pruning Deep Neural Networks Architectures with Evolution Strategy

Currently, Deep Neural Networks (DNNs) are used to solve all kinds of problems in the field of machine learning and artificial intelligence due to their learning and adaptation capabilities. However, most of the successful DNN models have a high computational complexity, which makes them difficult to deploy on mobile or embedded platforms. This has prompted many researchers to develop algorithms and approaches to help reduce the computational complexity of such models. One of them is called filter pruning where convolution filters are eliminated to reduce the number of parameters and, consequently, the computational complexity of the given model. In the present work, we propose a novel algorithm to perform filter pruning by using Multi-Objective Evolution Strategy (ES) algorithm, called DeepPruningES. Our approach avoids the need for using any knowledge during the pruning procedure and helps decision makers by returning three pruned DNN models with different trade-offs between performance and computational complexity. We show that DeepPruningES can significantly reduce a model's computational complexity by testing it on three DNN architectures: Convolutional Neural Networks, Residual Neural Networks, and Densely Connected Neural Networks.