REAL Reinforcement Learning: Planning with adversarial models

Model-Based Reinforcement Learning (MBRL) algorithms solve sequential decision-making problems, usually formalised as Markov Decision Processes, using a model of the environment dynamics to compute the optimal policy. When dealing with complex environments, the environment dynamics are frequently approximated with function approximators (such as...

Hierarchical Reinforcement Learning for Spatio-temporal Planning

Reinforcement learning (RL) is an area of Machine Learning (ML) concerned with learning how a software-defined agent should act in an environment to maximize the rewards. Similar to many ML methods, RL suffers from the curse of dimensionality, the exponential increase in solution space with the increase in problem dimensions. Learning the...