Research map / Reinforcement learning / Visual and representation RL
Meta-Reinforcement Learning of Structured Exploration Strategies
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add sampled per-episode latent noise to create temporally coherent exploration (ablation) | MAESN · Ant legged locomotion | trajectory spread | Helped |
| Use meta-learning to optimize latent parameters for fast policy-gradient adaptation, rather than learning a latent space without fast adaptation | MAESN · Novel tasks with sparse rewards: wheeled locomotion, legged locomotion, and object manipulation | reward during adaptation | Helped |
| Add a learned bias transformation to MAML (ablation) | MAML · Novel tasks | adaptation performance | Helped |
| Add adaptive step sizes and a bias transformation, adapting all parameters in the inner update (ablation) | MAML · Novel tasks | adaptation performance | Mixed |
| Add adaptive step sizes and a bias transformation, adapting only bias parameters in the inner update (ablation) | MAML · Novel tasks | adaptation performance | Mixed |
| Omit the policy-parameter inner-loop update during meta-training (ablation) | MAESN · Meta-training tasks | fast adaptation performance | Helped |
| Use a learned per-parameter step size instead of a fixed step size (ablation) | MAESN · Meta-training tasks | performance | Helped |
| Use reward shaping during meta-training, then evaluate on sparse rewards | MAESN and baselines · Novel tasks with sparse rewards | adaptation performance | Helped |
Closest papers by meaning
- Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables (ICML 2019 · similarity 0.89)
- Skill-based Meta-Reinforcement Learning (ICLR 2022 · similarity 0.89)
- First-Explore, then Exploit: Meta-Learning to Solve Hard Exploration-Exploitation Trade-Offs (NeurIPS 2024 · similarity 0.88)
- Decoupling Meta-Reinforcement Learning with Gaussian Task Contexts and Skills (AAAI 2024 · similarity 0.88)
- Discovering Temporally-Aware Reinforcement Learning Algorithms (ICLR 2024 · similarity 0.87)
- Can Learned Optimization Make Reinforcement Learning Less Difficult? (NeurIPS 2024 · similarity 0.87)
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