**NoisyNet-A3C** is a modification of [A3C](https://paperswithcode.com/method/a3c) that utilises noisy linear layers for exploration instead of 
$\epsilon$-greedy exploration as in the original [DQN](https://paperswithcode.com/method/dqn) formulation.

**Informative Sample Mining Network** is a multi-stage sample training scheme for GANs to reduce sample hardness while preserving sample informativeness. Adversarial Importance Weighting is proposed to select informative samples and assign them greater weight. The authors also propose Multi-hop Sample Training to avoid the potential problems in model training caused by sample mining. Based on the principle of divide-and-conquer, the authors produce target images by multiple hops, which means the image translation is decomposed into several separated steps.

Informative Sample Mining Network

Informative Sample Mining Network for Multi-Domain Image-to-Image Translation

NoisyNet-A3C

Noisy Networks for Exploration

**Soft Actor Critic (Autotuned Temperature** is a modification of the [SAC](https://paperswithcode.com/method/soft-actor-critic) reinforcement learning algorithm. [SAC](https://paperswithcode.com/method/sac) can suffer from brittleness to the temperature hyperparameter. Unlike in conventional reinforcement learning, where the optimal policy is independent of scaling of the reward function, in maximum entropy reinforcement learning the scaling factor has to be compensated by the choice a of suitable temperature, and a sub-optimal temperature can drastically degrade performance. To resolve this issue, SAC with Autotuned Temperature has an automatic gradient-based temperature tuning method that adjusts the expected entropy over the visited states to match a target value.

Source	Noisy Networks for Exploration
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com