Research map / Reinforcement learning / Visual and representation RL
Automatic Curriculum Learning through Value Disagreement
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| sample training goals by value-ensemble disagreement (VDS) instead of uniform sampling | HER with DDPG · 13 Fetch/Hand robotics tasks and 3 mazes | success rate / sample efficiency | Helped |
| use VDS instead of GoalGAN goal proposal | goal-conditioned RL · Ant navigation (2 environments) | success rate / sample efficiency | Helped |
| change sampling function f (exp, tanh, square) instead of identity (ablation) | VDS · Maze environments | success rate | No clear effect |
| increase value ensemble size from 3 to 10 (ablation) | VDS · Maze navigation | success rate | No clear effect |
| add VDS to vanilla DDPG without HER (ablation) | DDPG · two environments | success rate | Helped |
| combine VDS with HER versus HER alone (ablation) | HER · majority of environments | sample efficiency | Helped |
Closest papers by meaning
- Automated curriculum generation through setter-solver interactions (ICLR 2020 · similarity 0.91)
- Learning Progress Driven Multi-Agent Curriculum (ICML 2025 · similarity 0.88)
- Diffusion-based Curriculum Reinforcement Learning (NeurIPS 2024 · similarity 0.88)
- PORTAL: Automatic Curricula Generation for Multiagent Reinforcement Learning (AAAI 2024 · similarity 0.87)
- GoalLadder: Incremental Goal Discovery with Vision-Language Models (NeurIPS 2025 · similarity 0.87)
- Curriculum Design for Trajectory-Constrained Agent: Compressing Chain-of-Thought Tokens in LLMs (NeurIPS 2025 · similarity 0.86)
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