  Soft Actor-Critic (SAC) is an off-policy actor-critic deep reinforcement
learning (DRL) algorithm based on maximum entropy reinforcement learning. By
combining off-policy updates with an actor-critic formulation, SAC achieves
state-of-the-art performance on a range of continuous-action benchmark tasks,
outperforming prior on-policy and off-policy methods. The off-policy method
employed by SAC samples data uniformly from past experience when performing
parameter updates. We propose Emphasizing Recent Experience (ERE), a simple but
powerful off-policy sampling technique, which emphasizes recently observed data
while not forgetting the past. The ERE algorithm samples more aggressively from
recent experience, and also orders the updates to ensure that updates from old
data do not overwrite updates from new data. We compare vanilla SAC and
SAC+ERE, and show that ERE is more sample efficient than vanilla SAC for
continuous-action Mujoco tasks. We also consider combining SAC with Priority
Experience Replay (PER), a scheme originally proposed for deep Q-learning which
prioritizes the data based on temporal-difference (TD) error. We show that
SAC+PER can marginally improve the sample efficiency performance of SAC, but
much less so than SAC+ERE. Finally, we propose an algorithm which integrates
ERE and PER and show that this hybrid algorithm can give the best results for
some of the Mujoco tasks.
