Hi Mukherjee,
You can control the agent exploration by adjusting the entropy temperature options "EntropyWeightOptions" from the rlSACAgentOptions
For example, large values of EntropyWeight encourage the agent to explore the environment or control it by adjusting the temperature learning rate "LearnRate" to reach the target entropy "TargetEntropy" value [1]. In other words, you can use a fixed weight with zero learning rate and so on.
[1] Haarnoja, Tuomas, Aurick Zhou, Kristian Hartikainen, George Tucker, Sehoon Ha, Jie Tan, Vikash Kumar, et al. "Soft Actor-Critic Algorithms and Application." Preprint, submitted January 29, 2019. https://arxiv.org/abs/1812.05905.
