Intelligent Formation Control using Deep Reinforcement Learning

In this thesis, deep reinforcement learning is applied to the problem of formation control to enhance performance. The current state-of-the-art formation control algorithms are often not adaptive and require a high degree of expertise to tune. By introducing reinforcement learning in combination with a behavior-based formation control algorithm, simply tuning a reward function can change the entire dynamics of a group. In the experiments, a group of three agents moved to a goal which had its direct path blocked by obstacles. The degree of randomness in the environment varied: in some experiments, the obstacle positions and agent start positions were fixed between episodes, whereas in others they were completely random. The greatest improvements were seen in environments which did not change between episodes; in these experiments, agents could more than double their performance with regards to the reward. These results could be applicable to both simulated agents and physical agents operating in static areas, such as farms or warehouses. By adjusting the reward function, agents could improve the speed with which they approach a goal, obstacle avoidance, or a combination of the two. Two different and popular reinforcement algorithms were used in this work: Deep Double Q-Networks (DDQN) and Proximal Policy Optimization (PPO). Both algorithms showed similar success