MTLRL2019: ICML Workshop on Multi-Task and Lifelong Reinforcement Learning ICML 2019 Long Beach, CA, United States, June 10-15, 2019 |
Conference website | https://sites.google.com/view/mtlrl/home |
Submission link | https://easychair.org/conferences/?conf=mtlrl2019 |
Submission deadline | May 4, 2019 |
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 composition of subgoals or subtasks with shared structure
Multi-task and lifelong reinforcement learning have 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.
Submission Guidelines
The submitted work should be an extended abstract of between 4-8 pages (including references). The submission should be in pdf format and should follow the style guidelines for ICML 2019 (found here). The review process is double-blind and the work should be submitted by the latest May 3rd, 2019 (Anywhere on Earth). The submissions shouldn't have been previously published nor have appeared in the ICML main conference. Work currently under submission to another conference is welcome. There will be no formal publication of workshop proceedings. However, the accepted papers will be made available online on the workshop website.
Below are example topics that we welcome submissions from:
- 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 composition of subgoals or subtasks with shared structure
List of Topics
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.
Contact
All questions about submissions should be emailed to mtlrl@googlegroups.com