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Exploration-exploitation dilemma has long been a crucial issue inreinforcement learning. In this paper, we propose a new approach toautomatically balance between these two. Our method is built upon the SoftActor-Critic (SAC) algorithm, which uses an "entropy temperature" that balancesthe original task reward and the policy entropy, and hence controls thetrade-off between exploitation and exploration. It is empirically shown thatSAC is very sensitive to this hyperparameter, and the follow-up work (SAC-v2),which uses constrained optimization for automatic adjustment, has somelimitations. The core of our method, namely Meta-SAC, is to use metagradientalong with a novel meta objective to automatically tune the entropy temperaturein SAC. We show that Meta-SAC achieves promising performances on several of theMujoco benchmarking tasks, and outperforms SAC-v2 over 10% in one of the mostchallenging tasks, humanoid-v2.

Meta-SAC: Auto-tune the Entropy Temperature of Soft Actor-Critic via Metagradient