Policy Gradient is a reinforcement learning approach where the agent learns a policy directly by optimizing the probability of taking the best actions.
Unlike value-based methods like Q-Learning, policy gradient methods adjust policies using gradient ascent to maximize expected cumulative rewards.
How Policy Gradient Works
- Initialize Policy: Start with a random policy that maps states to action probabilities.
- Generate Episodes: Let the agent interact with the environment using the policy.
- Compute Rewards: Measure the total reward for each episode.
- Update Policy: Adjust the policy parameters to increase the probability of actions that lead to higher rewards using gradients.
- Repeat: Continue until the policy converges to an optimal strategy.
Advantages of Policy Gradient
- Can handle continuous action spaces
- Directly optimizes policies for maximum reward
- Works well with stochastic and complex environments
- Flexible for high-dimensional problems
Disadvantages
- High variance in gradient estimates
- Requires careful tuning of learning rate and batch sizes
- Slower convergence compared to some value-based methods
Real-World Examples
- Robotics control for precise movements
- Autonomous driving in dynamic environments
- Game AI for complex strategy optimization
- Trading algorithms adapting to market conditions
- Dialogue systems for conversational AI
Conclusion
Policy gradient methods are essential in reinforcement learning for directly improving action strategies. They are powerful tools for complex and continuous decision-making tasks.
Citations
https://savanka.com/category/learn/ai-and-ml/
https://www.w3schools.com/ai/