  In recent years, neural networks have enjoyed a renaissance as function
approximators in reinforcement learning. Two decades after Tesauro's TD-Gammon
achieved near top-level human performance in backgammon, the deep reinforcement
learning algorithm DQN achieved human-level performance in many Atari 2600
games. The purpose of this study is twofold. First, we propose two activation
functions for neural network function approximation in reinforcement learning:
the sigmoid-weighted linear unit (SiLU) and its derivative function (dSiLU).
The activation of the SiLU is computed by the sigmoid function multiplied by
its input. Second, we suggest that the more traditional approach of using
on-policy learning with eligibility traces, instead of experience replay, and
softmax action selection with simple annealing can be competitive with DQN,
without the need for a separate target network. We validate our proposed
approach by, first, achieving new state-of-the-art results in both stochastic
SZ-Tetris and Tetris with a small 10$\times$10 board, using TD($\lambda$)
learning and shallow dSiLU network agents, and, then, by outperforming DQN in
the Atari 2600 domain by using a deep Sarsa($\lambda$) agent with SiLU and
dSiLU hidden units.
