Significant progress has been made in reinforcement learning, enabling agents to accomplish complex tasks such as Atari games, robotic manipulation, simulated locomotion, and Go. These successes have stemmed from the core reinforcement learning formulation of learning a single policy or value function from scratch. However, reinforcement learning has proven challenging to scale to many practical real world problems due to problems in learning efficiency and objective specification, among many others. Recently, there has been emerging interest and research in leveraging structure and information across multiple reinforcement learning tasks to more efficiently and effectively learn complex behaviors. This includes: - curriculum and lifelong learning, where the problem requires learning a sequence of tasks, leveraging their shared structure to enable knowledge transfer - goal-conditioned reinforcement learning techniques that leverage the structure of the provided goal space to learn many tasks significantly faster - meta-learning methods that aim to learn efficient learning algorithms that can learn new tasks quickly - hierarchical reinforcement learning, where the reinforcement learning problem might entail a compositions of subgoals or subtasks with shared structure Multi-task and lifelong reinforcement learning has the potential to alter the paradigm of traditional reinforcement learning, to provide more practical and diverse sources of supervision, while helping overcome many challenges associated with reinforcement learning, such as exploration, sample efficiency and credit assignment. However, the field of multi-task and lifelong reinforcement learning is still young, with many more developments needed in terms of problem formulation, algorithmic and theoretical advances as well as better benchmarking and evaluation. The focus of this workshop will be on both the algorithmic and theoretical foundations of multi-task and lifelong reinforcement learning as well as the practical challenges associated with building multi-tasking agents and lifelong learning benchmarks. Our goal is to bring together researchers that study different problem domains (such as games, robotics, language, and so forth), different optimization approaches (deep learning, evolutionary algorithms, model-based control, etc.), and different formalisms (as mentioned above) to discuss the frontiers, open problems and meaningful next steps in multi-task and lifelong reinforcement learning.