Optimization
Grammatical evolution and Q-learning
Introduced by Custode et al. in Evolutionary learning of interpretable decision treesThis method works as a two-levels optimization algorithm. The outmost layer uses Grammatical evolution to evolve a grammar to build the agent. Then, Q-learning is used the fitness evaluation phase to allow the agent to learn to perform online learning.
Source: Evolutionary learning of interpretable decision trees
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Evolutionary Algorithms | 1 | 33.33% |
| OpenAI Gym | 1 | 33.33% |
| Reinforcement Learning (RL) | 1 | 33.33% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
