Context-Aware Composition of Agent Policies by Markov Decision Process Entity Embeddings and Agent Ensembles

1 code implementation • 28 Aug 2023 • Nicole Merkle, Ralf Mikut

Since the environments can be stochastic and complex in terms of the number of states and feasible actions, activities are usually modelled in a simplified way by Markov decision processes so that, e. g., agents with reinforcement learning are able to learn policies, that help to capture the context and act accordingly to optimally perform activities.

Entity Embeddings Knowledge Graphs +1