Policy Gradients in AI & Artificial Intelligence

In value-based methods like DQN, the agent learns 'how good is this state?' and then acts greedily. Policy Gradients (PG) cut out the state-value calculation. The network directly outputs a Probability Distribution over actions. During training, we use the total return to 'push' the probabilities of successful actions up and unsuccessful ones down. This is much closer to how humans learn skills—by trial and error and adjusting our future behavior based on the outcome.

The most basic PG algorithm is REINFORCE. It follows a simple logic: 1) Play a full episode. 2) For every step, calculate the gradient of the log-probability of the action taken. 3) Multiply that gradient by the total return. This ensures that actions leading to a $+10$ reward receive a huge 'boost' in probability, while actions leading to a $-10$ penalty are suppressed. Because it uses the full return, REINFORCE is unbiased but can be very high-variance.

Q-Learning struggles with Continuous Action Spaces because it's impossible to calculate the 'max' over an infinite number of actions. Policy Gradients thrive here. Instead of a discrete list, the network can output the parameters of a distribution (like the Mean and Standard Deviation of a Gaussian). The agent then samples from this distribution, allowing it to take precise, fluid actions like applying exactly $4.52$ Newtons of force to a motor.

Value functions tell us how good a state is. But sometimes, it's better to learn the action strategy directly. Policy Gradients (PG) allow us to optimize the 'Policy' itself using gradient descent.

The core idea is simple: if an action led to a high return, increase its probability. If it led to a loss, decrease its probability. We call this 'REINFORCE'.

Unlike Q-Learning, PG can handle 'Continuous Action Spaces' like steering a car or controlling a robotic joint with infinite precision.

Checkpoint: What is the main output of a Policy Gradient neural network?

• →Q-values for each action
• →A probability distribution over all possible actions

Policy Gradients are 'On-Policy'. They learn from the actions they are currently taking. This makes them more stable in some complex scenarios than value-based methods.

By mastering Policy Gradients, you unlock the ability to build AI that can move with the fluidity and precision of a living creature.

Checkpoint: Why are Policy Gradients preferred for continuous control (like a robot arm)?

• →They are smaller models
• →They can directly output continuous values instead of picking from a fixed list of actions

Policy Gradients mastered! You've learned to optimize behavior directly. Ready to combine values and policies with Actor-Critic methods?

Apply a Real Policy Gradient Step. Finish updating a policy weight by taking a small step in the direction of the gradient.