Research map / Reinforcement learning / Visual and representation RL
A New Representation of Successor Features for Transfer across Dissimilar Environments
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Model source successor features as noisy measurements of target successor features with Gaussian Processes, then use GPI | SFDE · Toy navigation maze | performance | Helped |
| Model source successor features as noisy measurements of target successor features with Gaussian Processes, then use GPI | SFDE · CartPole-v0 | performance | Helped |
| Model source successor features as noisy measurements of target successor features with Gaussian Processes, then use GPI | SFDE · FSF environment | average reward | Helped |
| Fine-tune source successor features on target observations | FSF · FSF environment, adaptation phase | average reward | Helped |
| Use linear projection to map source successor features to target successor features | LPSF · Toy navigation maze | speed of successor-feature updating | Hurt |
Closest papers by meaning
- Distributional Successor Features Enable Zero-Shot Policy Optimization (NeurIPS 2024 · similarity 0.89)
- Reward-Aware Proto-Representations in Reinforcement Learning (NeurIPS 2025 · similarity 0.89)
- Learning Successor Features the Simple Way (NeurIPS 2024 · similarity 0.88)
- A Distributional Analogue to the Successor Representation (ICML 2024 · similarity 0.88)
- SF-DQN: Provable Knowledge Transfer using Successor Feature for Deep Reinforcement Learning (ICML 2024 · similarity 0.87)
- Proto Successor Measure: Representing the Behavior Space of an RL Agent (ICML 2025 · similarity 0.86)
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