Research map / Learning theory and algorithms / Combinatorial and search optimization
Modeling and Optimization Trade-off in Meta-learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use domain randomized search (joint training) with PPO instead of MAML (ProMP) | Meta-RL initialization learning · Locomotion and MetaWorld environments excluding Half Cheetah Rand Vel and ML10 | post-update episodic reward | Helped |
| Use MAML instead of domain randomized search | Meta-RL with large sample sizes · Half Cheetah Rand Vel and ML10 | post-update episodic reward | Helped |
| Increase training sample size for MAML versus DRS | MAML vs DRS · ML45 | post-update episodic reward | Mixed |
| Combine MAML with TRPO instead of PPO (ablation) | MAML · Majority of meta-RL environments | post-update episodic reward | Helped |
| Use MAML instead of DRS for linear regression with small M and N | Meta-linear regression simulation (p=1) · Expected loss after meta-test optimization | probability MAML has lower expected loss | Hurt |
| Use MAML instead of DRS for linear regression with very large M and N | Meta-linear regression simulation (p=1) · Expected loss after meta-test optimization | probability MAML has lower expected loss | Helped |
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