ai_reinforcement_learning
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| Both sides previous revisionPrevious revisionNext revision | Previous revision | ||
| ai_reinforcement_learning [2025/05/29 18:48] – [Advanced Features] eagleeyenebula | ai_reinforcement_learning [2025/05/29 18:49] (current) – [Future Enhancements] eagleeyenebula | ||
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| 2. **Custom Metrics API**: | 2. **Custom Metrics API**: | ||
| - | * Extend the `evaluate_agent()` to include custom performance indicators such as time steps, penalties, average Q-values, and success rates. | + | * Extend the **evaluate_agent()** to include custom performance indicators such as time steps, penalties, average Q-values, and success rates. |
| 3. **Environment Swapping**: | 3. **Environment Swapping**: | ||
| Line 222: | Line 222: | ||
| * **Policy-Gradient Support**: | * **Policy-Gradient Support**: | ||
| - | Add native support for policy-gradient algorithms like PPO and A3C. | + | Add native support for policy-gradient algorithms like **PPO** and **A3C**. |
| * **Distributed RL Training**: | * **Distributed RL Training**: | ||
| - | Introduce multi-agent or distributed training environments for large-scale RL scenarios. | + | Introduce multi-agent or distributed training environments for **large-scale RL** scenarios. |
| * **Visualization Dashboards**: | * **Visualization Dashboards**: | ||
| Line 231: | Line 231: | ||
| * **Recurrent Architectures**: | * **Recurrent Architectures**: | ||
| - | Incorporate LSTM or GRU-based RL for handling temporal dependencies. | + | Incorporate |
| ===== Conclusion ===== | ===== Conclusion ===== | ||
ai_reinforcement_learning.1748544487.txt.gz · Last modified: 2025/05/29 18:48 by eagleeyenebula