Reproduce “Curriculum Generation under Structured Parametric Environments for Robust Navigation Policies” on OpenResearch

About this paper

Prishita Ray

A reparameterized curriculum generation framework, Reparam, enables robust navigation policies by using unidirectional gradient-based optimization over continuous environment parameters. The framework, particularly with distribution-shift regularization (Reparam-M), improved performance in complex environments like Car Racing and Bipedal Walker, outperforming various curriculum and non-curriculum baselines.

Key insights

• Unidirectional curriculum updates, where difficulty only increases when the policy is proficient, are more effective than bidirectional updates for promoting robust learning and preventing stagnation.
• Distribution-shift regularization (MSE-based) is particularly beneficial for complex, multimodal observation spaces (like image inputs) to enhance stability and latent representation quality.
• Gradually expanding the curriculum difficulty while retaining exposure to easier tasks helps mitigate catastrophic forgetting and supports sustained learning progress.

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