Continuous Control - DDPG, TD3, SAC

This continues the Q-Learning notes, where the policy stayed implicit as the $\arg\max$ of $Q$ over a finite set of actions. Here the actions go continuous, so that max becomes its own optimization and we bring an explicit actor back to approximate it. That lands us in DDPG, TD3, and SAC on the off-policy actor-critic branch.