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Soft Actor-Critic (SAC) is an off-policy actor-critic reinforcement learningalgorithm, essentially based on entropy regularization. SAC trains a policy bymaximizing the trade-off between expected return and entropy (randomness in thepolicy). It has achieved state-of-the-art performance on a range ofcontinuous-control benchmark tasks, outperforming prior on-policy andoff-policy methods. SAC works in an off-policy fashion where data are sampleduniformly from past experiences (stored in a buffer) using which parameters ofthe policy and value function networks are updated. We propose certain crucialmodifications for boosting the performance of SAC and make it more sampleefficient. In our proposed improved SAC, we firstly introduce a newprioritization scheme for selecting better samples from the experience replaybuffer. Secondly we use a mixture of the prioritized off-policy data with thelatest on-policy data for training the policy and the value function networks.We compare our approach with the vanilla SAC and some recent variants of SACand show that our approach outperforms the said algorithmic benchmarks. It iscomparatively more stable and sample efficient when tested on a number ofcontinuous control tasks in MuJoCo environments.

Improved Soft Actor-Critic: Mixing Prioritized Off-Policy Samples with On-Policy Experience