Research map / Reinforcement learning / Visual and representation RL
Meta-Reinforcement Learning Based on Self-Supervised Task Representation Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use trajectory-level task inference instead of updating the task representation at each time step (ablation) | MoSS · Cheetah-Vel-OOD and Cheetah-Multi-Task | average return | Hurt |
| Replace the Gaussian mixture with a single-component Gaussian (ablation) | MoSS · Cheetah-Multi-Task | average return | Hurt |
| Remove contrastive learning (ablation) | MoSS · Cheetah-Vel-OOD and Cheetah-Multi-Task | average return | Hurt |
| Replace Bayes-adaptive planning with posterior sampling (ablation) | MoSS · Cheetah-Vel-OOD and Cheetah-Multi-Task | average return | Hurt |
Closest papers by meaning
- Entropy Regularized Task Representation Learning for Offline Meta-Reinforcement Learning (AAAI 2025 · similarity 0.93)
- Towards an Information Theoretic Framework of Context-Based Offline Meta-Reinforcement Learning (NeurIPS 2024 · similarity 0.92)
- MetaCARD: Meta-Reinforcement Learning with Task Uncertainty Feedback via Decoupled Context-Aware Reward and Dynamics Components (AAAI 2024 · similarity 0.91)
- Decoupling Meta-Reinforcement Learning with Gaussian Task Contexts and Skills (AAAI 2024 · similarity 0.91)
- Generalizable Task Representation Learning for Offline Meta-Reinforcement Learning with Data Limitations (AAAI 2024 · similarity 0.91)
- Scrutinize What We Ignore: Reining In Task Representation Shift Of Context-Based Offline Meta Reinforcement Learning (ICLR 2025 · similarity 0.90)
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