Research map / Reinforcement learning / Visual and representation RL
Reinforcement Learning with Prototypical Representations
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use prototype-stratified candidates for nearest-neighbor entropy instead of a random replay-buffer batch | Proto-RL · Intrinsic reward computation | variance | Helped |
| Add intrinsic reward (ablation) | Proto-RL · Downstream exploration | exploration speed | Helped |
| Stop gradients from the exploration RL agent into the representation | Proto-RL · Generalization | generalization | Helped |
Closest papers by meaning
- Foundation Policies with Hilbert Representations (ICML 2024 · similarity 0.89)
- The Surprising Ineffectiveness of Pre-Trained Visual Representations for Model-Based Reinforcement Learning (NeurIPS 2024 · similarity 0.88)
- Towards Principled Representation Learning from Videos for Reinforcement Learning (ICLR 2024 · similarity 0.88)
- Bridging State and History Representations: Understanding Self-Predictive RL (ICLR 2024 · similarity 0.88)
- Enabling Adaptive Agent Training in Open-Ended Simulators by Targeting Diversity (NeurIPS 2024 · similarity 0.88)
- Proto Successor Measure: Representing the Behavior Space of an RL Agent (ICML 2025 · similarity 0.88)
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