KaigeNot Another Entry-Level Tutorial of Reinforcement LearningThis is a series of posts on ‘advanced bits’ of practical reinforcement learning. Topics will be covered:May 23May 23
KaigePyTorch Version of Life-Time Value PredictionA DEEP PROBABILISTIC MODEL FOR CUSTOMER LIFETIME VALUE PREDICTIONApr 27Apr 27
KaigePaper Reading: Imitation Learning with Concurrent Actions in 3D GamesThis paper is from SEED Team Electronic Arts. In this article, we put quotations from this paper to be a summaryApr 18Apr 18
KaigePaper Reading: Proto-Value-NetworksProto-Value Networks: Scaling Representation Learning with Auxiliary TasksApr 13Apr 13
KaigeHow to learn successor representations from data?This article summaries the methods in the literature o how to learn successor representations from dataApr 1Apr 1
KaigePaper Reading: Generalization successor features to continuous domains for multi-task learningAs the title shows, this paper demonstrate how to use the learned successor feature in continuous multi-task learning. I put some key…Mar 27Mar 27
KaigePaper Reading: Learning Successor States and Goal-Dependent Values: A mathematical ViewpointThis paper is a deep analysis on successor representation (SR). I put some key insights and points from this paper.Mar 26Mar 26
KaigeNot Another Intrinsic RewardThe following papers made some interesting arguments on intrinsic rewards for exploration. I put them here for reference.Mar 22Mar 22
KaigePaper Reading: What About Inputting Policy in Value Function?In actor-critic RL agent, the value function is used to predict the state value under the current policy and then used to guide the policy…Mar 22Mar 22