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--- ai_reinforcement_learning [2025/05/29 18:47] – [Conclusion] eagleeyenebula
+++ ai_reinforcement_learning [2025/05/29 18:49] (current) – [Future Enhancements] eagleeyenebula
@@ Line 190: / Line 190: @@
 . **Dynamic Training Integration**:
-     * Use dynamic algorithms (e.g., DQN, PPO, A3C) with custom logic through modular training loops.
+     * Use dynamic algorithms (e.g., **DQN**, **PPO**, **A3C**) with custom logic through modular training loops.
 . **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.
 . **Environment Swapping**:
-     * Seamlessly swap between default environments (e.g., CartPole, LunarLander) and custom-designed RL environments.
+     * Seamlessly swap between default environments (e.g., **CartPole**, **LunarLander**) and custom-designed **RL environments**.
 ===== Use Cases =====
@@ Line 222: / Line 222: @@
   * **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**:
-    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**:
@@ Line 231: / Line 231: @@
   * **Recurrent Architectures**:
-    Incorporate LSTM or GRU-based RL for handling temporal dependencies.
+    Incorporate **LSTM** or **GRU-based RL** for handling temporal dependencies.
 ===== Conclusion =====