Evolutionary Discovery of Reinforcement Learning Algorithms via Large Language Models

1. 1Define the search space of possible update rules
2. 2Use large language models as generative variation operators to explore the search space
3. 3Evaluate the performance of discovered algorithms using a reinforcement learning environment
4. 4Select and refine the most promising algorithms through evolutionary iteration

ML researchers and AI engineers can benefit from this approach to automate the discovery of new reinforcement learning algorithms, improving the efficiency of their research and development process

💡 Evolutionary frameworks can be used to automate the discovery of reinforcement learning algorithms

💡 Discovering new RL algorithms with large language models!