ePoster · Robotics
Learning an environment model in real-time with core knowledge and closed-loop behaviours
Giulia Lafratta, Bernd Porr, Christopher Chandler, Alice Miller · Bernstein Conference 2024
In autonomous navigation, an agent's knowledge of its environment is represented as sensory inputs (states) linked together by actions. A popular approach for acquiring this knowledge is reinforcement learning (RL), where learning is stereotyped, i.e. an agent learns to react to a state with the action associated with maximum reward. Model training requires many trials and occurs offline, where the agent aims to learn generalisable reflexes rather than to reason about its surroundings actively. Thus, RL agents are inefficient to train and can exhibit inflexible behaviour. Indeed, behavioural f