Learning and Adaptation: A Comparison of Methods in Case of Navigation in an Artificial Robot World
 Neural networks, reinforcement learning systems and evolutionary
algorithms are widely used to solve problems in real-world robotics. We
investigate learning and adaptation capabilities of agents and show
that the learning time required in continual learning is shorter than
that of learning from scratch under various learning conditions. We
argue that agents using appropriate hybridization of learning and
evolutionary algorithms show better learning and adaptation capability
as compared to agents using learning algorithms only. We support our
argument with experiments, where agents learn optimal policies in an
artificial robot world.