Abstract:Reinforcement Learning (RL) methods often rely on gradient estimates to learn an optimal policy for control problems. These expensive computations result in long training times, a poor rate of convergence, and sample inefficiency when applied to real-world problems with a large state and action space. Evolutionary Computation (EC)-based techniques offer a gradient-free apparatus to train a deep neural network for RL problems. In this work, we leverage the benefits of EC and propose a novel variant of genetic a… Show more
“…The second paper A scalable species-based genetic algorithm for reinforcement learning problems by Seth et al . (2022) proposes a novel genetic algorithm (GA) variant called species-based GA (SP-GA) which utilizes a species-inspired weight initialization strategy and trains a population of deep neural networks, each estimating the Q-function for the RL problem. The authors’ results on Atari 2600 games demonstrate that the performance of SP-GA is comparable with gradient-based algorithms like deep Q-network, asynchronous advantage actor critic and gradient-free algorithms like evolution strategy (ES) and simple GA while requiring far fewer hyperparameters to train.…”
Section: Contents Of the Special Issuementioning
confidence: 99%
“…The second paper A scalable species-based genetic algorithm for reinforcement learning problems by Seth et al (2022) proposes a novel genetic algorithm (GA) variant called species-based GA (SP-GA) which utilizes a species-inspired weight initialization strategy and trains a population of deep neural Cite this article: K. Mason and P. Mannion. Special issue on evolutionary machine learning.…”
“…The second paper A scalable species-based genetic algorithm for reinforcement learning problems by Seth et al . (2022) proposes a novel genetic algorithm (GA) variant called species-based GA (SP-GA) which utilizes a species-inspired weight initialization strategy and trains a population of deep neural networks, each estimating the Q-function for the RL problem. The authors’ results on Atari 2600 games demonstrate that the performance of SP-GA is comparable with gradient-based algorithms like deep Q-network, asynchronous advantage actor critic and gradient-free algorithms like evolution strategy (ES) and simple GA while requiring far fewer hyperparameters to train.…”
Section: Contents Of the Special Issuementioning
confidence: 99%
“…The second paper A scalable species-based genetic algorithm for reinforcement learning problems by Seth et al (2022) proposes a novel genetic algorithm (GA) variant called species-based GA (SP-GA) which utilizes a species-inspired weight initialization strategy and trains a population of deep neural Cite this article: K. Mason and P. Mannion. Special issue on evolutionary machine learning.…”
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