SAC: The Best Algorithm for Continuous Control

This is Part 5 — the finale of our Reinforcement Learning series. We're covering the state-of-the-art algorithm for continuous control.

Series Overview:

• Part 1: RL Basics — MDP, Bellman Equation, Value Functions
• Part 2: From Q-Learning to DQN
• Part 3: Policy Gradient Methods
• Part 4: PPO — The Industry Standard
• Part 5: SAC — Mastering Continuous Control (You are here)

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Why SAC?

PPO is great, but it has a weakness: sample efficiency. As an on-policy algorithm, PPO throws away data after each update. For robotics and real-world systems where each interaction is expensive, this is a major limitation.

Soft Actor-Critic (SAC) (Haarnoja et al., 2018) addresses this with three key ideas:

1. Off-policy learning — reuse all past experience via replay buffer
2. Entropy maximization — explore as much as possible while maximizing reward
3. Automatic temperature tuning — balance exploration and exploitation automatically

SAC is the go-to algorithm for continuous control, dominating benchmarks in robotic manipulation, locomotion, and dexterous hand tasks.

The Maximum Entropy Framework

Standard RL maximizes expected reward:

π* = argmax E [ Σ γ^t · r_t ]

SAC maximizes expected reward plus entropy:

π* = argmax E [ Σ γ^t · (r_t + α · H(π(·|s_t))) ]

where H(π) = -E[log π(a|s)] is the entropy of the policy, and α (alpha) is the temperature parameter controlling the exploration-exploitation tradeoff.

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