What Is RL, Why Is It So Hard, and How Does It Go Wrong?
Prior reading: Gradient Descent and Backpropagation | Loss Functions and Spaces The Setup An agent takes actions in an environment, receives rewards, and learns a policy $\pi(a|s)$ that maximizes expected cumulative reward. $$\max_\pi \mathbb{E}\left[\sum_{t=0}^T \gamma^t r_t\right]$$ Where Are the Neural Nets? Neural networks serve as function approximators for things that are too complex to represent exactly: Policy network: $\pi_\theta(a|s)$ — maps states to action probabilities Value network: $V_\phi(s)$ — estimates expected future reward from a state Q-network: $Q_\psi(s,a)$ — estimates expected future reward from a state-action pair World model (optional): $p_\omega(s'|s,a)$ — predicts next state Without neural nets, RL only works for tiny state spaces (tabular methods). Neural nets let it scale to images, language, and continuous control. ...