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Automatic Curriculum Learning For Deep RL: A Short Survey
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Automatic Curriculum Learning (ACL) has become a cornerstone of recent successes in Deep Reinforcement Learning (DRL).These methods shape the learning trajectories of agents by challenging them with tasks adapted to their capacities. In recent years, they have been used to improve sample efficiency and asymptotic performance, to organize exploration, to encourage generalization or to solve sparse reward problems, among others. The ambition of this work is dual: 1) to present a compact and accessible introduction to the Automatic Curriculum Learning literature and 2) to draw a bigger picture of the current state of the art in ACL to encourage the cross-breeding of existing concepts and the emergence of new ideas.
Forward citations
Cited by 11 Pith papers
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Echoverse: Deep, Evolving Environments for Training Computer-Use Agents at Scale
Deep, capability-targeted, co-evolving synthetic environments raise a 9B computer-use agent from 36.5% to 67.1% and enable RL gains the live web cannot supply.
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LEACL: LLM-Enhanced Automatic Curriculum Learning for Reinforcement Learning in Long-Horizon Manipulation Tasks
LLMs generate subtask decompositions and ACL task spaces so sparse-reward RL solves long-horizon manipulation better than dense human rewards on five LIBERO tasks.
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Self-Modifying Lean Proof Agents with Verifier-Grounded Benchmark Coevolution
A verifier-grounded self-evolving Lean proof agent with a champion-driven, self-hardening benchmark reached 45.1% held-out miniF2F solve rate versus 32.0% for a fixed-benchmark baseline.
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Exploring Multimodal AI Reasoning for Meteorological Forecasting from Skew-T Diagrams
A 250M-parameter vision-language model fine-tuned on Skew-T diagrams achieves CSI comparable to IFS-HRES for 3-hour precipitation probability in South Korean summer.
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Efficient Skill Discovery via Regret-Aware Optimization
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Energy-Based Transfer for Reinforcement Learning
An energy-based out-of-distribution score gates a teacher's action advice in reinforcement learning, so guidance is given only in states the teacher has seen during training.
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EvoCurr: Self-evolving Curriculum with Behavior Code Generation for Complex Decision-making
EvoCurr couples an LLM curriculum designer with an LLM code-generating solver, but its only reported success is 1 of 5 runs and no direct baseline is shown.
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GACL: Grounded Adaptive Curriculum Learning with Active Task and Performance Monitoring
GACL adds domain grounding via alternating reference and synthetic task sampling to a VAE-based regret-driven curriculum teacher, reporting higher success rates than CLUTR on BARN navigation and quadruped locomotion i...
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ADEPT: Adaptive Diffusion Environment for Policy Transfer Sim-to-Real
An adaptive curriculum that steers a pretrained diffusion terrain generator via policy success-weighted latent blending improves zero-shot sim-to-real off-road navigation performance.
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Reactive Aerobatic Flight via Reinforcement Learning
An RL policy trained with an automated curriculum and domain randomization lets a quadrotor perform continuous inverted flight while reactively navigating a moving gate.
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