Curiosity Driven Deep Reinforcement Learning for Motion Planning in Multiagent Environment Motion planning in dynamic multiagent environment tends to be a challenging feat by itself. If the agent is also required to complete an elaborate task the complexity of the problem substantially increases. Heuristically designing policies for unstructured environments can be unfeasible and time consuming for certain scenarios. We propose a Deep Reinforcement Learning approach for a continuous multiagent setting that is robust enough to handle highlevel task accomplishment and lowlevel collision avoidance control. To alleviate disadvantages of a sparse reward environment we introduce intrinsic reward inspired by the curious behavior of animals and humans. As for the lowlevel, collision avoidance segment of control, we introduce a general reward function. Furthermore, we formulate an agentcentric state space and implement robust Reinforcement Learning algorithm that is capable of handling reward signal from both sources. Suitable 3D physical environment is constructed to examine the feasibility of our approach. Subsequently, case study is performed and effectiveness of our method is validated when compared with the stateoftheart Reinforcement Learning algorithm.

Adaptive LeaderFollower Formation Control and Obstacle Avoidance via Deep Reinforcement Learning We propose a deep reinforcement learning (DRL) methodology for the tracking, obstacle avoidance, and formation control of nonholonomic robots. By separating visionbased control into a perception module and a controller module, we can train a DRL agent without sophisticated physics or 3D modeling. In addition, the modular framework averts daunting retrains of an imagetoaction endtoend neural network, and provides flexibility in transferring the controller to different robots. First, we train a convolutional neural network (CNN) to accurately localize in an indoor setting with dynamic foregroundbackground. Then, we design a new DRL algorithm named Momentum Policy Gradient (MPG) for continuous control tasks and prove its convergence. We also show that MPG is robust at tracking varying leader movements and can naturally be extended to problems of formation control. Leveraging reward shaping, features such as collision and obstacle avoidance can be easily integrated into a DRL controller.