Research map / Reinforcement learning / Imitation learning
Distance Minimization for Reward Learning from Scored Trajectories
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| quantize expert scores into only 5 bins (ablation) | DM-IRL · Satellite terrain, small feature pool | learning score f(w): mean f(w) of over 0.8 with 5 bins | Hurt |
| add up to 100 percent multiplicative scoring noise (eta=2) (ablation) | DM-IRL · Satellite terrain, small feature pool | learning score f(w): almost 0.8 after 9 demonstrations | Hurt |
| increase scoring noise beyond eta=0.25 with 63,000 features (ablation) | LASSO DM-IRL · Satellite terrain, large feature pool | learning score f(w) | Hurt |
| use L1 (LASSO) regularization when features far outnumber trajectories | DM-IRL · Satellite terrain, large feature pool | learning score f(w): f(w) > 0.88 after 30 noiseless demonstrations | Helped |
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