Research map / Reinforcement learning / Visual and representation RL
Adapt to Environment Sudden Changes by Learning a Context Sensitive Policy
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use ESCP's variance minimization, determinant maximization, and truncated-history context encoder | Context-based meta-RL · Grid-world with sudden changes | adaptation speed: 10× faster | Helped |
| Add variance minimization to encourage fast, stable context encoding (ablation) | ESCP · Sudden environment changes | context identification speed | Helped |
| Add relational matrix determinant maximization to separate environment encodings (ablation) | ESCP · Sudden environment changes | environment encoding quality | Helped |
| Truncate the context encoder's history to focus on recent interactions (ablation) | ESCP · Sudden environment changes | adaptation speed | Helped |
Closest papers by meaning
- MetaCARD: Meta-Reinforcement Learning with Task Uncertainty Feedback via Decoupled Context-Aware Reward and Dynamics Components (AAAI 2024 · similarity 0.89)
- CERTAIN: Context Uncertainty-aware One-Shot Adaptation for Context-based Offline Meta Reinforcement Learning (ICML 2025 · similarity 0.88)
- Scrutinize What We Ignore: Reining In Task Representation Shift Of Context-Based Offline Meta Reinforcement Learning (ICLR 2025 · similarity 0.88)
- Distilling Reinforcement Learning Algorithms for In-Context Model-Based Planning (ICLR 2025 · similarity 0.88)
- Learning to Reuse Policies in State Evolvable Environments (ICML 2025 · similarity 0.87)
- Decoupling Meta-Reinforcement Learning with Gaussian Task Contexts and Skills (AAAI 2024 · similarity 0.87)
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