  It is vital to accurately estimate the value function in Deep Reinforcement
Learning (DRL) such that the agent could execute proper actions instead of
suboptimal ones. However, existing actor-critic methods suffer more or less
from underestimation bias or overestimation bias, which negatively affect their
performance. In this paper, we reveal a simple but effective principle: proper
value correction benefits bias alleviation, where we propose the
generalized-activated weighting operator that uses any non-decreasing function,
namely activation function, as weights for better value estimation.
Particularly, we integrate the generalized-activated weighting operator into
value estimation and introduce a novel algorithm, Generalized-activated Deep
Double Deterministic Policy Gradients (GD3). We theoretically show that GD3 is
capable of alleviating the potential estimation bias. We interestingly find
that simple activation functions lead to satisfying performance with no
additional tricks, and could contribute to faster convergence. Experimental
results on numerous challenging continuous control tasks show that GD3 with
task-specific activation outperforms the common baseline methods. We also
uncover a fact that fine-tuning the polynomial activation function achieves
superior results on most of the tasks.
