Reinforcement Learning & Decision Systems

Share Your Expertise

Reinforcement learning and decision systems are revolutionizing autonomous systems, robotics, and strategic optimization. Ready Tensor provides a platform for practitioners to showcase their innovations, from classical RL approaches to cutting-edge deep RL solutions. Share your work to inspire others and gain recognition for your contributions to this rapidly evolving field.

Areas of Interest

Core Approaches:

• Value-based methods

• Policy gradient algorithms

• Actor-critic architectures

• Model-based RL

• Multi-agent systems

• Inverse reinforcement learning

• Imitation learning

• Hierarchical RL

Advanced Techniques:

• Deep reinforcement learning

• Off-policy learning

• Meta-learning for RL

• Distributed RL systems

• Safe RL approaches

• Transfer learning in RL

Application Domains:

• Robotics and control

• Game playing agents

• Resource allocation

• Portfolio optimization

• Autonomous vehicles

• Industrial automation

We welcome all innovative approaches in reinforcement learning and decision systems, from theoretical advances to practical implementations solving real-world challenges.

Types of Publications

From traditional dynamic programming to modern deep RL approaches, we welcome all types of reinforcement learning and decision system publications. Whether you're working on single-agent RL, multi-agent systems, or hybrid approaches, share your work to help advance the field of autonomous decision-making.

Research Contributions:

• Original research in RL algorithms

• Surveys of decision-making techniques

• Comparative studies of RL methods

• Novel RL environments and benchmarks

• Reproducibility studies of published methods

Implementation & Applications:

• Industry applications of RL

• Game-playing agents

• Robotics control systems

• Trading strategies

• Energy management solutions

Open Source & Independent Projects:

• Open-source RL libraries

• Custom environments

• Algorithm implementations

• Multi-agent frameworks

• Training infrastructure

Educational & Academic:

• Academic research implementations

• RL tutorials and guides

• Course projects across different tasks

• Training environments

• Algorithm visualizations

How to Publish

1. Create a free Ready Tensor account if you don't already have one.

2. To initiate your publication, click 'Start Publication' from the top menu.

3. Follow the intuitive workflow. You can copy/paste content in markdown style, and even upload content from your Jupyter Notebooks. It's fast and easy.

4. Tag your publication with relevant keywords like 'reinforcement-learning', 'deep-rl', or 'decision-systems' to help others discover your work.

5. Run automated publication assessment to get instant feedback on your documentation quality.

6. Preview your work and click 'Publish' to share it with the community.

7. Note you can edit your documentation even after publication. You do not need to unpublish for editing. All changes are applied automatically.

8. To maximize visibility to your publication, use the social media links to share on platforms like LinkedIn, X, and more.