A fundamental concept in Reinforcement Learning (RL), Policy Gradients directly optimize the policy by learning a parameterized strategy through gradient ascent. Unlike value-based methods, this approach focuses on maximizing the expected cumulative reward by adjusting the policy's parameters.
Key Principles
Direct Policy Optimization
- The policy is represented as a probability distribution over actions, parameterized by a neural network.
- Gradients of the expected reward with respect to the policy parameters are computed and used to update the model.
- 📈 Example: In games like Game_AI, the policy learns to choose optimal moves through trial and error.
Gradient Estimation
- Uses REINFORCE algorithm or Actor-Critic frameworks to estimate gradients.
- 🧠 Example: Neural_Networks enable complex policy representations, such as deep deterministic policy gradients.
Advantages & Challenges
- ✅ Pros: Handles continuous action spaces, scalable for high-dimensional problems.
- ⚠️ Cons: High variance in gradient estimates, requires careful exploration strategies.
Applications
- 🤖 Robotics: Training robots to perform tasks through policy gradients.
- 🎮 Game AI: Reinforcement learning for game agents (e.g., Game_AI).
- 🚀 Optimization: Policy gradients in continuous control environments.
For deeper insights, explore our guide on Reinforcement_Learning or Deep_Learning_Frameworks.