Overview
The NeurIPS 2019 Workshop on Biological and Artificial Reinforcement Learning is currently accepting paper submissions. This workshop aims to bridge the gap between biological and artificial systems in reinforcement learning, providing a platform for researchers to share insights and advancements in this critical area of study.
Background & Relevance
Reinforcement learning (RL) is a pivotal area in both artificial intelligence and cognitive science. Understanding how biological systems learn and adapt can inform the development of more sophisticated artificial agents. This workshop will explore various aspects of RL, including comparisons between biological and artificial agents, which is essential for advancing both theoretical and practical applications in AI.
Key Details
- Submission Deadline: September 7, 2019, 11:59 PM Pacific Time
- Location: NeurIPS 2019
- Topics of Interest:
- Benchmark tasks comparing biological and artificial agents
- Hierarchical reinforcement learning and skill learning
- Inductive biases and priors in RL
- Representations used in reinforcement learning
- Model-based and model-free learning approaches
- Lifelong learning strategies
- Intrinsic motivation and learning without external rewards
- The role of memory in learning processes
- Submission Link: NeurIPS Workshop Submission
- Contact Email: BiologicalArtificialRL@gmail.com
Eligibility & Participation
This call for papers is open to researchers and practitioners in the fields of reinforcement learning, cognitive science, and neuroscience. It targets individuals interested in the intersection of biological and artificial learning systems.
Submission or Application Guidelines
Participants are invited to submit papers that adhere to the NeurIPS 2019 formatting guidelines. Each submission should not exceed five pages, excluding references and appendices. The workshop will only accept original work that has not been published in the main NeurIPS conference, though previously published work from other relevant venues is welcome.
Additional Context / Real-World Relevance
The exploration of biological and artificial reinforcement learning is crucial for advancing AI technologies. Insights gained from biological systems can lead to more effective learning algorithms and improved AI applications in various fields, including robotics, healthcare, and cognitive computing. This workshop represents a significant opportunity for researchers to contribute to this evolving dialogue.
Conclusion
Researchers are encouraged to participate in this workshop by submitting their original work. Engaging with this community can foster collaboration and innovation in the field of reinforcement learning. Explore this opportunity to share your findings and connect with fellow researchers in the AI/ML domain.
Category: CFP & Deadlines
Tags: reinforcement learning, neurips, biological agents, artificial agents, cognitive science, neuroscience, machine learning, skill learning
