Why DDPG Still Matters When SAC and TD3 Exist
Most practitioners reach for SAC or TD3 when they need continuous control. They're right to do so — both algorithms fix critical flaws in DDPG. But here's the thing: you can't truly understand why SAC's entropy regularization works or why TD3's twin critics matter until you've debugged DDPG's failure modes firsthand.
I'm not advocating for using DDPG in production. I'm saying that building it from scratch teaches you the architecture patterns that underpin modern off-policy RL. The replay buffer that lets you reuse experience. The target networks that stabilize Q-value estimates. The deterministic policy that sidesteps the exploration-exploitation tradeoff (badly, but instructively).
This post walks through a complete DDPG implementation in ~400 lines of PyTorch. We'll hit the Pendulum-v1 environment from Gymnasium 0.29.1, watch the agent fail spectacularly with bad hyperparameters, then fix it. No handwaving — just working code and honest observations about what broke along the way.
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