Lane change is a challenging task which requires delicate actions to ensure safety and comfort. Some recent studies have attempted to solve the lane-change control problem with Reinforcement Learning (RL), yet the action is confined to discrete action space. To overcome this limitation, we formulate the lane change behavior with continuous action in a model-free dynamic driving environment based on Deep Deterministic Policy Gradient (DDPG). The reward function, which is critical for learning the optimal policy, is defined by control values, position deviation status, and maneuvering time to provide the RL agent informative signals. The RL agent is trained from scratch without resorting to any prior knowledge of the environment and vehicle dynamics since they are not easy to obtain. Seven models under different hyperparameter settings are compared. A video showing the learning progress of the driving behavior is available. It demonstrates the RL vehicle agent initially runs out of road boundary frequently, but eventually has managed to smoothly and stably change to the target lane with a success rate of 100% under diverse driving situations in simulation.

The arrival of the robotic arm was not a happy affair at Professional Finishing in Richmond, California, just across the bay from San Francisco. In contrast to the hulking factory arms that have traditionally labored in isolation, this robot was meant to work right alongside humans, delicately sanding and painting things like speaker cases or cabinets for medical devices. Which sounded a lot like a first step toward replacing the company's workers altogether.

The second "Computers Gone Wild: Impact and Implications of Developments in Artificial Intelligence on Society" workshop took place on February 19, 2016 at Harvard Law School. Marin Solja?i?, Max Tegmark, Bruce Schneier, and Jonathan Zittrain convened this informal workshop to discuss recent advancements in artificial intelligence research. Participants represented a wide range of expertise and perspectives and discussed four main topics during the day-long event: the impact of artificial intelligence on labor and economics, algorithmic decision-making, particularly in law, autonomous weapons, and the risks of emergent human-level artificial intelligence. Each session opened with a brief overview of the existing literature related to the topic from a designated participant, followed by remarks from two or three provocateurs. The session leader then moderated a discussion with the larger group.