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Energetic Natural Gradient Descent
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace Fisher information metric with energy information matrix (energetic natural gradient), using d = |q(s,a1)-q(s,a2)| | policy search on mountain car · Mountain car one-step update | performance after one update: around 30% higher than Fisher at random policy | Helped |
| Use ordinary gradient (G = I) instead of energetic natural gradient | policy search on mountain car · Mountain car one-step update | performance after one update | Hurt |
| Gap between energetic and Fisher natural gradient shrinks as initial policy performance increases | policy search on mountain car · Mountain car one-step update | advantage of energetic over Fisher | Mixed |
| Choose d so that EIM equals FIM (d = 1(w1≠w2)(p(w1)+p(w2))/2) | energetic natural gradient · theory | equivalence to Fisher | No clear effect |
| Set d so EIM equals Hessian (d(ω1,ω2)=0.5) on toy problem, giving Newton direction | energetic natural gradient on illustrative 3-outcome example · illustrative f∘p | direction of update | No clear effect |
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