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This thesis explores the application of reinforcement learning (RL) in optimal truss design within civil engineering, modeling the design synthesis process as a finite Markov Decision Process (MDP). The study demonstrates that RL can effectively automate the selection of optimal designs by maximizing cumulative rewards based on performance improvements. Results indicate the potential for extending this approach to more complex design criteria, highlighting RL
- Author
- Lê Quỳnh
- Language
- EN