Soft actor critic – Deep reinforcement learning with real-world robots

We are announcing the release of our state-of-the-art off-policy model-free reinforcement learning algorithm, soft actor-critic (SAC). This algorithm has been developed jointly at UC Berkeley and Google Brain, and we have been using it internally for our robotics experiment. Soft actor-critic is, to our knowledge, one of the most efficient model-free algorithms available today, making it especially well-suited for real-world robotic learning. We also release our implementation of SAC, which is particularly designed for real-world robotic systems. What makes an ideal deep RL algorithm for real-world systems?