Towards Learning Multiagent Negotiations via SelfPlay. Making sophisticated, robust, and safe sequential decisions is at the heart of intelligent systems. This is especially critical for planning in complex multiagent environments, where agents need to anticipate other agentsu0027 intentions and possible future actions. Traditional methods formulate the problem as a Markov Decision Process, but the solutions often rely on various assumptions and become brittle when presented with corner cases. In contrast, deep reinforcement learning (Deep RL) has been very effective at finding policies by simultaneously exploring, interacting, and learning from environments. Leveraging the powerful Deep RL paradigm, we demonstrate that an iterative procedure of selfplay can create progressively more diverse environments, leading to the learning of sophisticated and robust multiagent policies. We demonstrate this in a challenging multiagent simulation of merging traffic, where agents must interact and negotiate with others in order to successfully merge on or off the road. While the environment starts off simple, we increase its complexity by iteratively adding an increasingly diverse set of agents to the agent zoo as training progresses. Qualitatively, we find that through selfplay, our policies automatically learn interesting behaviors such as defensive driving, overtaking, yielding, and the use of signal lights to communicate intentions to other agents. In addition, quantitatively, we show a dramatic improvement of the success rate of merging maneuvers from 63% to over 98%.

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.