Research map / Reinforcement learning / Visual and representation RL
Enhanced Meta Reinforcement Learning via Demonstrations in Sparse Reward Environments
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Combine RL-based task adaptation with behavior cloning from demonstrations | Meta-RL in sparse-reward environments · Point2D Navigation, TwoWheeled Locomotion, HalfCheetah Forward-Backward | average return | Helped |
| Warm-start the meta-policy with a behavior-cloning update before task adaptation (ablation) | EMRLD · Sparse-reward simulated environments | average return | Helped |
| Use behavior cloning for meta-optimization instead of RL on validation rollouts (ablation) | EMRLD · Sparse-reward environments | average return | Hurt |
| Add an additional behavior-cloning gradient during meta-parameter update (ablation) | EMRLD · Sparse-reward environments | average return | No clear effect |
| Use demonstration data for supervised learning in both adaptation and meta-optimization | Meta-BC · Sparse-reward simulated environments | average return | Hurt |
| Rely on RL-only adaptation without demonstration guidance | MAML · Sparse-reward simulated environments | average return | Hurt |
| Use demonstrations for supervised meta-optimization while retaining RL adaptation | GMPS · Sparse-reward simulated environments | average return | Hurt |
| Combine RL and imitation to learn from truncated, sub-optimal demonstrations | EMRLD · Point2D Navigation, TwoWheeled Locomotion, HalfCheetah Forward-Backward | average return | Helped |
| Use demonstration-guided adaptation for tasks with unknown dynamics drift | EMRLD and EMRLD-WS · TurtleBot real-world drift adaptation | goal-reaching performance | Helped |
Closest papers by meaning
- Guided Meta-Policy Search (NeurIPS 2019 · similarity 0.93)
- DynaMITE-RL: A Dynamic Model for Improved Temporal Meta-Reinforcement Learning (NeurIPS 2024 · similarity 0.91)
- Enhancing Online Reinforcement Learning with Meta-Learned Objective from Offline Data (AAAI 2025 · similarity 0.91)
- MetaCARD: Meta-Reinforcement Learning with Task Uncertainty Feedback via Decoupled Context-Aware Reward and Dynamics Components (AAAI 2024 · similarity 0.91)
- Learning Process Rewards via Visitation Matching for Efficient RL (NeurIPS 2026 · similarity 0.90)
- Meta-Reinforcement Learning with Universal Policy Adaptation: Provable Near-Optimality under All-task Optimum Comparator (NeurIPS 2024 · similarity 0.90)
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