Reinforcement Learning and Advanced Reinforcement Learning to Improve Autonomous Vehicle Planning

Planning for autonomous vehicles is a challenging process that involves navigating through dynamic and unpredictable surroundings while making judgments in real-time. Traditional planning methods sometimes rely on predetermined rules or customized heuristics, which could not generalize well to various driving conditions. In this article, we provide a unique framework to enhance autonomous vehicle planning by fusing conventional RL methods with cutting-edge reinforcement learning techniques. To handle many elements of planning issues, our system integrates cutting-edge algorithms including deep reinforcement learning, hierarchical reinforcement learning, and meta-learning. Our framework helps autonomous vehicles make decisions that are more reliable and effective by utilizing the advantages of these cutting-edge strategies.With the use of the RLTT technique, an autonomous vehicle can learn about the intentions and preferences of human drivers by inferring the underlying reward function from expert behaviour that has been seen. The autonomous car can make safer and more human-like decisions by learning from expert demonstrations about the fundamental goals and limitations of driving. Large-scale simulations and practical experiments can be carried out to gauge the effectiveness of the suggested approach. On the basis of parameters like safety, effectiveness, and human likeness, the autonomous vehicle planning system's performance can be assessed. The outcomes of these assessments can help to inform future developments and offer insightful information about the strengths and weaknesses of the strategy

Adaptive and learning-based formation control of swarm robots

Autonomous aerial and wheeled mobile robots play a major role in tasks such as search and rescue, transportation, monitoring, and inspection. However, these operations are faced with a few open challenges including robust autonomy, and adaptive coordination based on the environment and operating conditions, particularly in swarm robots with limited communication and perception capabilities. Furthermore, the computational complexity increases exponentially with the number of robots in the swarm. This thesis examines two different aspects of the formation control problem. On the one hand, we investigate how formation could be performed by swarm robots with limited communication and perception (e.g., Crazyflie nano quadrotor). On the other hand, we explore human-swarm interaction (HSI) and different shared-control mechanisms between human and swarm robots (e.g., BristleBot) for artistic creation. In particular, we combine bio-inspired (i.e., flocking, foraging) techniques with learning-based control strategies (using artificial neural networks) for adaptive control of multi- robots. We first review how learning-based control and networked dynamical systems can be used to assign distributed and decentralized policies to individual robots such that the desired formation emerges from their collective behavior. We proceed by presenting a novel flocking control for UAV swarm using deep reinforcement learning. We formulate the flocking formation problem as a partially observable Markov decision process (POMDP), and consider a leader-follower configuration, where consensus among all UAVs is used to train a shared control policy, and each UAV performs actions based on the local information it collects. In addition, to avoid collision among UAVs and guarantee flocking and navigation, a reward function is added with the global flocking maintenance, mutual reward, and a collision penalty. We adapt deep deterministic policy gradient (DDPG) with centralized training and decentralized execution to obtain the flocking control policy using actor-critic networks and a global state space matrix. In the context of swarm robotics in arts, we investigate how the formation paradigm can serve as an interaction modality for artists to aesthetically utilize swarms. In particular, we explore particle swarm optimization (PSO) and random walk to control the communication between a team of robots with swarming behavior for musical creation