Research map / Reinforcement learning / Imitation learning
Maximum Likelihood Constraint Inference for Inverse Reinforcement Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Infer constraints by greedily selecting minimal constraints that eliminate the most probability mass without excluding any demonstration | Maximum Likelihood Constraint Inference · Synthetic grid world | constraint recovery | Helped |
| Use a lower KL-divergence stopping threshold | Maximum Likelihood Constraint Inference · Synthetic grid world | false positive rate | Hurt |
| Increase the number of demonstrations | Maximum Likelihood Constraint Inference · Synthetic grid world | false positive rate | Helped |
| Infer state constraints from human navigation demonstrations | Maximum Likelihood Constraint Inference · Human obstacle avoidance | obstacle prediction | Helped |
| Decrease the KL-divergence threshold to infer more obstacle states | Maximum Likelihood Constraint Inference · Human obstacle avoidance | constraint coverage | Mixed |
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