AIRL — Adversarial Inverse Reinforcement Learning
We introduce a practical GAN-style IRL algorithm named adversarial inverse reinforcement learning(AIRL)
GAN-GCL
We build a connection between maximum entropy inverse reinforcement learning and generative adversarial networks
GCL — Guided Cost Learning
We introduce a maximum entropy inverse reinforcement learning algorithm, named guided policy learning.
PCL — Path Consistency Learning and More
Discussion on path consistency learning and its derivatives.
SAC — Soft Actor-Critic with Adaptive Temperature
We introduce adaptive temperature to soft actor-critic(SAC).
SAC — Soft Actor-Critic
Discussion on soft actor-critic, a maximum entropy algorithm.
SVI — Soft Value Iteration
We address the optimism problem of the probabilistic graphical model introduced in the previous post via variational inference.
PGM — Probabilistic Graphic Model
Discussion on statistic inference in a temporal probabilistic graphical model.
SL — Statistic Learning: A Connection to Neural Networks
We expand the topic of latent variable models in a sense that the latent variables model the underlying structure of the observed data, whereby the model is able to do statistical inference over these latent variables. Then we will build a connnection between statistic learning and neural networks.