From Pixels to Digital Agents: An Empirical Study on the Taxonomy and Technological Trends of Reinforcement Learning Environments
#reinforcement learning #digital agents #taxonomy #technological trends #empirical study #AI environments #simulation
📌 Key Takeaways
- The study analyzes the evolution of reinforcement learning environments from pixel-based to agent-focused systems.
- It proposes a taxonomy to categorize different types of RL environments based on empirical data.
- Technological trends in RL environments are identified, highlighting shifts toward more complex and interactive simulations.
- The research provides insights into how environment design impacts the development and performance of digital agents.
📖 Full Retelling
arXiv:2603.23964v1 Announce Type: new
Abstract: The remarkable progress of reinforcement learning (RL) is intrinsically tied to the environments used to train and evaluate artificial agents. Moving beyond traditional qualitative reviews, this work presents a large-scale, data-driven empirical investigation into the evolution of RL environments. By programmatically processing a massive corpus of academic literature and rigorously distilling over 2,000 core publications, we propose a quantitative
🏷️ Themes
Reinforcement Learning, AI Environments
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Original Source
arXiv:2603.23964v1 Announce Type: new
Abstract: The remarkable progress of reinforcement learning (RL) is intrinsically tied to the environments used to train and evaluate artificial agents. Moving beyond traditional qualitative reviews, this work presents a large-scale, data-driven empirical investigation into the evolution of RL environments. By programmatically processing a massive corpus of academic literature and rigorously distilling over 2,000 core publications, we propose a quantitative
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