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openai.com·14h ago
AI Research
arXiv CS.AI·May 25·8 min read
Curriculum reinforcement learning with measurable task representation learning
TL;DR
Curriculum reinforcement learning (CRL) trains agents on progressively harder tasks, but existing methods fail in complex navigation because task spaces aren't Euclidean (don't follow flat geometry). Researchers built a variational autoencoder that learns task representations in a latent space where distance meaningfully measures task similarity via rewards and state transitions.
✦ Why It Matters
Engineers can apply measurable latent task representations to automatically generate curricula for complex navigation and control problems without manual task design.
Key Takeaways
- Use VAE to encode rewards and state transitions for meaningful task similarity metrics in non-Euclidean spaces.
- Automatic curriculum generation via latent space interpolation outperforms manual task sequencing and GAN-based methods.
- Latent task embeddings enable measuring task distance without assuming Euclidean geometry.
- Apply this approach to navigation and control tasks where standard distance metrics fail.
Full Summary
Curriculum reinforcement learning (CRL) trains agents by sequencing tasks from easy to hard, accumulating knowledge to solve a target task. Prior work either manually sequences tasks or generates intermediate tasks by interpolating between initial and target task distributions using distance metrics.
However, navigation tasks exist in non-Euclidean spaces where standard distance metrics fail. Researchers proposed a variational autoencoder (VAE) that encodes reward functions and state transitions to produce latent task representations where proximity correlates with task similarity.
Using this learned representation, they developed an automatic curriculum generation scheme that progressively creates tasks increasingly similar to the target. Experiments on challenging navigation tasks demonstrated superior performance compared to state-of-the-art interpolation-based and generative adversarial network (GAN) approaches, with results published in Neural Networks (2026).