Improving Sample Efficiency in Evolutionary RL using Off-policy Ranking
--Dr. Gugan Thoppe-- Reinforcement learning is a type of machine learning technique where the agent learns from the interactive environment by trial and error and has applications in sectors such as self-driving cars, marketing, robotics, gaming, manufacturing etc. However, at times it is difficult to apply reinforcement learning in situations where objective function features such as linearity, convexity or differentiability often are non-existent or difficult to detect. This is where Evolutionary strategy RL can help.
3 min read
CombSGPO: A new algorithm to protect wildlife
Poaching and illegal smuggling of wildlife have remained a cause of concern for wildlife authorities. As per the World Wide Fund for Nature (WWF), Wildlife trade poses the second-biggest direct threat to the survival of species after habitat destruction. As per the study conducted by TRAFFIC, a leading wildlife trade monitoring networking of the World Wide Fund for Nature (WWF), around 1,11,312 individual tortoises or freshwater turtles (11,000 a year) have been illegally traded across India since 2009.
3 min read
Finding Influencers in Social Networks: Reinforcement Learning Shows the Way
Social Network Analysis has given us many tools to effectively manage information dissemination in a social group, study growth and dynamics in such groups, etc. But one of the key challenges when studying social groups of underprivileged or socially marginalized groups is the recovery of the underlying social network itself. This study proposes a machine learning approach for learning to effectively allocate a limited budget to discover the network.
5 min read