Run v2
This commit is contained in:
Johnny Fernandes
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Config loaded from config.json
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Config: {'W_PER_SHEEP': 2.0, 'W_ALIGN': 0.05, 'W_PEN_BONUS': 10.0, 'W_COMPLETE': 100.0, 'W_STEP_COST': 0.02, 'W_COMPACT': 0.0, 'W_WALL_TOUCH': 0.0, 'WALL_TOUCH_BUFFER': 0.4, 'ALIGN_SHAPE': 'standoff', 'ALIGN_GATED': True, 'ENTRY_AWARE': True, 'ent_coef': 0.02}
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Run dir: runs/v2
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Curriculum: 1 → 10 sheep, 1,500,000 steps/stage
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[Stage n_sheep=1] training 1,500,000 steps
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... [1 sheep | 100,000 steps | ret(last 40)=-23.39 win_sr=8% cum_sr=8%]
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... [1 sheep | 200,000 steps | ret(last 50)=-22.10 win_sr=10% cum_sr=9%]
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... [1 sheep | 300,000 steps | ret(last 50)=-23.02 win_sr=10% cum_sr=10%]
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... [1 sheep | 400,000 steps | ret(last 50)=-18.97 win_sr=18% cum_sr=12%]
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... [1 sheep | 500,000 steps | ret(last 50)=-20.01 win_sr=8% cum_sr=11%]
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... [1 sheep | 600,000 steps | ret(last 50)=-18.57 win_sr=14% cum_sr=12%]
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... [1 sheep | 700,000 steps | ret(last 50)=-17.55 win_sr=22% cum_sr=14%]
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... [1 sheep | 800,000 steps | ret(last 50)=+7.41 win_sr=66% cum_sr=23%]
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... [1 sheep | 900,000 steps | ret(last 50)=+17.61 win_sr=100% cum_sr=47%]
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... [1 sheep | 1,000,000 steps | ret(last 50)=+16.11 win_sr=100% cum_sr=65%]
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... [1 sheep | 1,100,000 steps | ret(last 50)=+15.82 win_sr=100% cum_sr=74%]
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... [1 sheep | 1,200,000 steps | ret(last 50)=+14.33 win_sr=100% cum_sr=80%]
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... [1 sheep | 1,300,000 steps | ret(last 50)=+14.19 win_sr=100% cum_sr=84%]
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... [1 sheep | 1,400,000 steps | ret(last 50)=+14.00 win_sr=100% cum_sr=87%]
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... [1 sheep | 1,500,000 steps | ret(last 50)=+13.96 win_sr=100% cum_sr=89%]
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[Stage n_sheep=1] evaluating 30 eps
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[Stage n_sheep=1] sr=100% mean_len=234 mean_min_pen=3.7m mean_act=0.41
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failure modes: SUCCESS=30
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reward/step: progress=+0.1118 alignment=+0.0003 compact=+0.0000 wall_touch=+0.0000 pen_bonus=+0.0427 step_cost=-0.0200 complete=+0.4274
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[Stage n_sheep=2] training 1,500,000 steps
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... [2 sheep | 1,507,336 steps | ret(last 0)=+nan win_sr=nan% cum_sr=nan%]
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... [2 sheep | 1,607,336 steps | ret(last 40)=-4.45 win_sr=8% cum_sr=8%]
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... [2 sheep | 1,707,336 steps | ret(last 50)=-4.56 win_sr=8% cum_sr=9%]
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... [2 sheep | 1,807,336 steps | ret(last 50)=-2.33 win_sr=12% cum_sr=10%]
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... [2 sheep | 1,907,336 steps | ret(last 50)=+1.93 win_sr=24% cum_sr=14%]
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... [2 sheep | 2,007,336 steps | ret(last 50)=+7.32 win_sr=52% cum_sr=24%]
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... [2 sheep | 2,107,336 steps | ret(last 50)=+10.52 win_sr=58% cum_sr=30%]
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... [2 sheep | 2,207,336 steps | ret(last 50)=+15.67 win_sr=76% cum_sr=39%]
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... [2 sheep | 2,307,336 steps | ret(last 50)=+16.91 win_sr=78% cum_sr=46%]
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... [2 sheep | 2,407,336 steps | ret(last 50)=+21.91 win_sr=96% cum_sr=53%]
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... [2 sheep | 2,507,336 steps | ret(last 50)=+21.08 win_sr=94% cum_sr=60%]
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... [2 sheep | 2,607,336 steps | ret(last 50)=+20.24 win_sr=92% cum_sr=65%]
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... [2 sheep | 2,707,336 steps | ret(last 50)=+21.40 win_sr=96% cum_sr=70%]
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... [2 sheep | 2,807,336 steps | ret(last 50)=+21.95 win_sr=100% cum_sr=73%]
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... [2 sheep | 2,907,336 steps | ret(last 50)=+20.73 win_sr=100% cum_sr=76%]
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... [2 sheep | 3,007,336 steps | ret(last 50)=+21.25 win_sr=100% cum_sr=79%]
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[Stage n_sheep=2] evaluating 30 eps
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[Stage n_sheep=2] sr=87% mean_len=1064 mean_min_pen=4.1m mean_act=0.59
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failure modes: SUCCESS=26 COMPACT_CANT_DRIVE=4
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reward/step: progress=+0.0565 alignment=+0.0071 compact=+0.0000 wall_touch=+0.0000 pen_bonus=+0.0163 step_cost=-0.0200 complete=+0.0815
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[Stage n_sheep=3] training 1,500,000 steps
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... [3 sheep | 3,014,664 steps | ret(last 0)=+nan win_sr=nan% cum_sr=nan%]
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... [3 sheep | 3,114,664 steps | ret(last 50)=+17.60 win_sr=72% cum_sr=73%]
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... [3 sheep | 3,214,664 steps | ret(last 50)=+25.44 win_sr=98% cum_sr=87%]
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... [3 sheep | 3,314,664 steps | ret(last 50)=+25.73 win_sr=92% cum_sr=90%]
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... [3 sheep | 3,414,664 steps | ret(last 50)=+28.01 win_sr=98% cum_sr=92%]
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... [3 sheep | 3,514,664 steps | ret(last 50)=+25.71 win_sr=94% cum_sr=93%]
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... [3 sheep | 3,614,664 steps | ret(last 50)=+24.73 win_sr=94% cum_sr=93%]
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... [3 sheep | 3,714,664 steps | ret(last 50)=+23.51 win_sr=88% cum_sr=92%]
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... [3 sheep | 3,814,664 steps | ret(last 50)=+25.11 win_sr=96% cum_sr=93%]
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... [3 sheep | 3,914,664 steps | ret(last 50)=+27.02 win_sr=100% cum_sr=93%]
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... [3 sheep | 4,014,664 steps | ret(last 50)=+24.67 win_sr=94% cum_sr=94%]
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... [3 sheep | 4,114,664 steps | ret(last 50)=+26.08 win_sr=98% cum_sr=94%]
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... [3 sheep | 4,214,664 steps | ret(last 50)=+26.69 win_sr=98% cum_sr=94%]
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... [3 sheep | 4,314,664 steps | ret(last 50)=+24.01 win_sr=92% cum_sr=94%]
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... [3 sheep | 4,414,664 steps | ret(last 50)=+25.74 win_sr=98% cum_sr=94%]
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... [3 sheep | 4,514,664 steps | ret(last 50)=+27.43 win_sr=100% cum_sr=95%]
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[Stage n_sheep=3] evaluating 30 eps
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[Stage n_sheep=3] sr=100% mean_len=769 mean_min_pen=3.5m mean_act=0.72
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failure modes: SUCCESS=30
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reward/step: progress=+0.1121 alignment=+0.0078 compact=+0.0000 wall_touch=+0.0000 pen_bonus=+0.0390 step_cost=-0.0200 complete=+0.1301
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[Stage n_sheep=4] training 1,500,000 steps
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... [4 sheep | 4,521,992 steps | ret(last 0)=+nan win_sr=nan% cum_sr=nan%]
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... [4 sheep | 4,621,992 steps | ret(last 50)=+32.50 win_sr=100% cum_sr=96%]
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... [4 sheep | 4,721,992 steps | ret(last 50)=+31.21 win_sr=100% cum_sr=98%]
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... [4 sheep | 4,821,992 steps | ret(last 50)=+34.05 win_sr=100% cum_sr=99%]
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... [4 sheep | 4,921,992 steps | ret(last 50)=+32.04 win_sr=100% cum_sr=99%]
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... [4 sheep | 5,021,992 steps | ret(last 50)=+29.20 win_sr=100% cum_sr=99%]
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... [4 sheep | 5,121,992 steps | ret(last 50)=+31.56 win_sr=100% cum_sr=99%]
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... [4 sheep | 5,221,992 steps | ret(last 50)=+31.25 win_sr=100% cum_sr=100%]
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... [4 sheep | 5,321,992 steps | ret(last 50)=+30.62 win_sr=100% cum_sr=100%]
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... [4 sheep | 5,421,992 steps | ret(last 50)=+30.44 win_sr=100% cum_sr=100%]
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... [4 sheep | 5,521,992 steps | ret(last 50)=+32.84 win_sr=100% cum_sr=100%]
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... [4 sheep | 5,621,992 steps | ret(last 50)=+30.98 win_sr=100% cum_sr=100%]
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... [4 sheep | 5,721,992 steps | ret(last 50)=+28.77 win_sr=98% cum_sr=100%]
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... [4 sheep | 5,821,992 steps | ret(last 50)=+29.24 win_sr=100% cum_sr=100%]
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... [4 sheep | 5,921,992 steps | ret(last 50)=+30.83 win_sr=100% cum_sr=100%]
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... [4 sheep | 6,021,992 steps | ret(last 50)=+30.06 win_sr=100% cum_sr=100%]
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[Stage n_sheep=4] evaluating 30 eps
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[Stage n_sheep=4] sr=100% mean_len=750 mean_min_pen=3.5m mean_act=1.23
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failure modes: SUCCESS=30
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reward/step: progress=+0.1586 alignment=+0.0113 compact=+0.0000 wall_touch=+0.0000 pen_bonus=+0.0533 step_cost=-0.0200 complete=+0.1334
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[Stage n_sheep=5] training 1,500,000 steps
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... [5 sheep | 6,029,320 steps | ret(last 0)=+nan win_sr=nan% cum_sr=nan%]
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... [5 sheep | 6,129,320 steps | ret(last 50)=+31.97 win_sr=100% cum_sr=100%]
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... [5 sheep | 6,229,320 steps | ret(last 50)=+32.32 win_sr=100% cum_sr=100%]
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... [5 sheep | 6,329,320 steps | ret(last 50)=+34.26 win_sr=100% cum_sr=100%]
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... [5 sheep | 6,429,320 steps | ret(last 50)=+33.75 win_sr=100% cum_sr=100%]
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... [5 sheep | 6,529,320 steps | ret(last 50)=+34.77 win_sr=100% cum_sr=100%]
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... [5 sheep | 6,629,320 steps | ret(last 50)=+34.06 win_sr=100% cum_sr=100%]
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... [5 sheep | 6,729,320 steps | ret(last 50)=+32.39 win_sr=96% cum_sr=100%]
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... [5 sheep | 6,829,320 steps | ret(last 50)=+32.33 win_sr=100% cum_sr=100%]
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... [5 sheep | 6,929,320 steps | ret(last 50)=+33.29 win_sr=100% cum_sr=100%]
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... [5 sheep | 7,029,320 steps | ret(last 50)=+32.12 win_sr=100% cum_sr=100%]
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... [5 sheep | 7,129,320 steps | ret(last 50)=+32.58 win_sr=100% cum_sr=100%]
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... [5 sheep | 7,229,320 steps | ret(last 50)=+33.27 win_sr=100% cum_sr=100%]
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... [5 sheep | 7,329,320 steps | ret(last 50)=+33.64 win_sr=100% cum_sr=100%]
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... [5 sheep | 7,429,320 steps | ret(last 50)=+32.67 win_sr=100% cum_sr=100%]
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... [5 sheep | 7,529,320 steps | ret(last 50)=+32.79 win_sr=100% cum_sr=100%]
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[Stage n_sheep=5] evaluating 30 eps
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[Stage n_sheep=5] sr=97% mean_len=921 mean_min_pen=3.2m mean_act=1.33
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failure modes: SUCCESS=29 PARTIAL_3of5=1
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reward/step: progress=+0.1565 alignment=+0.0135 compact=+0.0000 wall_touch=+0.0000 pen_bonus=+0.0536 step_cost=-0.0200 complete=+0.1050
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[Stage n_sheep=6] training 1,500,000 steps
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... [6 sheep | 7,536,648 steps | ret(last 0)=+nan win_sr=nan% cum_sr=nan%]
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... [6 sheep | 7,636,648 steps | ret(last 50)=+35.93 win_sr=100% cum_sr=96%]
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... [6 sheep | 7,736,648 steps | ret(last 50)=+37.56 win_sr=100% cum_sr=97%]
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... [6 sheep | 7,836,648 steps | ret(last 50)=+34.93 win_sr=100% cum_sr=98%]
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... [6 sheep | 7,936,648 steps | ret(last 50)=+32.71 win_sr=98% cum_sr=98%]
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... [6 sheep | 8,036,648 steps | ret(last 50)=+36.84 win_sr=100% cum_sr=99%]
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... [6 sheep | 8,136,648 steps | ret(last 50)=+35.11 win_sr=100% cum_sr=99%]
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... [6 sheep | 8,236,648 steps | ret(last 50)=+36.54 win_sr=100% cum_sr=99%]
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... [6 sheep | 8,336,648 steps | ret(last 50)=+34.67 win_sr=100% cum_sr=99%]
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... [6 sheep | 8,436,648 steps | ret(last 50)=+36.14 win_sr=100% cum_sr=99%]
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... [6 sheep | 8,536,648 steps | ret(last 50)=+36.95 win_sr=100% cum_sr=99%]
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... [6 sheep | 8,636,648 steps | ret(last 50)=+35.42 win_sr=100% cum_sr=99%]
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... [6 sheep | 8,736,648 steps | ret(last 50)=+33.44 win_sr=100% cum_sr=100%]
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... [6 sheep | 8,836,648 steps | ret(last 50)=+36.70 win_sr=100% cum_sr=100%]
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... [6 sheep | 8,936,648 steps | ret(last 50)=+34.03 win_sr=100% cum_sr=100%]
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... [6 sheep | 9,036,648 steps | ret(last 50)=+34.53 win_sr=100% cum_sr=100%]
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[Stage n_sheep=6] evaluating 30 eps
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[Stage n_sheep=6] sr=97% mean_len=1193 mean_min_pen=3.4m mean_act=1.36
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failure modes: SUCCESS=29 COMPACT_CANT_DRIVE=1
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reward/step: progress=+0.1597 alignment=+0.0173 compact=+0.0000 wall_touch=+0.0000 pen_bonus=+0.0492 step_cost=-0.0200 complete=+0.0810
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[Stage n_sheep=7] training 1,500,000 steps
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... [7 sheep | 9,043,976 steps | ret(last 0)=+nan win_sr=nan% cum_sr=nan%]
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... [7 sheep | 9,143,976 steps | ret(last 50)=+40.54 win_sr=100% cum_sr=100%]
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... [7 sheep | 9,243,976 steps | ret(last 50)=+38.70 win_sr=98% cum_sr=99%]
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... [7 sheep | 9,343,976 steps | ret(last 50)=+38.13 win_sr=100% cum_sr=100%]
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... [7 sheep | 9,443,976 steps | ret(last 50)=+40.37 win_sr=100% cum_sr=100%]
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... [7 sheep | 9,543,976 steps | ret(last 50)=+39.40 win_sr=100% cum_sr=99%]
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... [7 sheep | 9,643,976 steps | ret(last 50)=+40.44 win_sr=98% cum_sr=99%]
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... [7 sheep | 9,743,976 steps | ret(last 50)=+37.74 win_sr=100% cum_sr=99%]
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... [7 sheep | 9,843,976 steps | ret(last 50)=+39.91 win_sr=98% cum_sr=99%]
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... [7 sheep | 9,943,976 steps | ret(last 50)=+40.67 win_sr=100% cum_sr=99%]
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... [7 sheep | 10,043,976 steps | ret(last 50)=+35.38 win_sr=100% cum_sr=99%]
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... [7 sheep | 10,143,976 steps | ret(last 50)=+38.31 win_sr=100% cum_sr=99%]
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... [7 sheep | 10,243,976 steps | ret(last 50)=+40.86 win_sr=100% cum_sr=99%]
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... [7 sheep | 10,343,976 steps | ret(last 50)=+40.95 win_sr=100% cum_sr=99%]
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... [7 sheep | 10,443,976 steps | ret(last 50)=+37.90 win_sr=100% cum_sr=99%]
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... [7 sheep | 10,543,976 steps | ret(last 50)=+39.07 win_sr=100% cum_sr=99%]
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[Stage n_sheep=7] evaluating 30 eps
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[Stage n_sheep=7] sr=100% mean_len=1209 mean_min_pen=3.2m mean_act=1.37
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failure modes: SUCCESS=30
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reward/step: progress=+0.1774 alignment=+0.0179 compact=+0.0000 wall_touch=+0.0000 pen_bonus=+0.0579 step_cost=-0.0200 complete=+0.0827
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[Stage n_sheep=8] training 1,500,000 steps
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... [8 sheep | 10,551,304 steps | ret(last 0)=+nan win_sr=nan% cum_sr=nan%]
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... [8 sheep | 10,651,304 steps | ret(last 50)=+42.81 win_sr=100% cum_sr=100%]
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... [8 sheep | 10,751,304 steps | ret(last 50)=+44.59 win_sr=100% cum_sr=100%]
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... [8 sheep | 10,851,304 steps | ret(last 50)=+45.59 win_sr=98% cum_sr=99%]
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... [8 sheep | 10,951,304 steps | ret(last 50)=+42.27 win_sr=98% cum_sr=99%]
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... [8 sheep | 11,051,304 steps | ret(last 50)=+45.05 win_sr=98% cum_sr=99%]
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... [8 sheep | 11,151,304 steps | ret(last 50)=+45.50 win_sr=100% cum_sr=99%]
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... [8 sheep | 11,251,304 steps | ret(last 50)=+43.60 win_sr=100% cum_sr=99%]
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... [8 sheep | 11,351,304 steps | ret(last 50)=+40.26 win_sr=100% cum_sr=99%]
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... [8 sheep | 11,451,304 steps | ret(last 50)=+43.00 win_sr=100% cum_sr=99%]
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... [8 sheep | 11,551,304 steps | ret(last 50)=+43.16 win_sr=100% cum_sr=100%]
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... [8 sheep | 11,651,304 steps | ret(last 50)=+42.78 win_sr=100% cum_sr=100%]
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... [8 sheep | 11,751,304 steps | ret(last 50)=+42.32 win_sr=98% cum_sr=99%]
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... [8 sheep | 11,851,304 steps | ret(last 50)=+41.62 win_sr=100% cum_sr=99%]
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... [8 sheep | 11,951,304 steps | ret(last 50)=+42.56 win_sr=98% cum_sr=99%]
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... [8 sheep | 12,051,304 steps | ret(last 50)=+41.83 win_sr=100% cum_sr=99%]
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[Stage n_sheep=8] evaluating 30 eps
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[Stage n_sheep=8] sr=100% mean_len=1492 mean_min_pen=3.2m mean_act=1.38
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failure modes: SUCCESS=30
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reward/step: progress=+0.1916 alignment=+0.0190 compact=+0.0000 wall_touch=+0.0000 pen_bonus=+0.0536 step_cost=-0.0200 complete=+0.0670
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[Stage n_sheep=9] training 1,500,000 steps
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... [9 sheep | 12,058,632 steps | ret(last 0)=+nan win_sr=nan% cum_sr=nan%]
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... [9 sheep | 12,158,632 steps | ret(last 50)=+46.03 win_sr=100% cum_sr=100%]
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... [9 sheep | 12,258,632 steps | ret(last 50)=+46.87 win_sr=96% cum_sr=97%]
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... [9 sheep | 12,358,632 steps | ret(last 50)=+45.48 win_sr=96% cum_sr=97%]
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... [9 sheep | 12,458,632 steps | ret(last 50)=+47.02 win_sr=96% cum_sr=97%]
|
||||||
|
... [9 sheep | 12,558,632 steps | ret(last 50)=+44.66 win_sr=96% cum_sr=97%]
|
||||||
|
... [9 sheep | 12,658,632 steps | ret(last 50)=+46.60 win_sr=96% cum_sr=97%]
|
||||||
|
... [9 sheep | 12,758,632 steps | ret(last 50)=+41.85 win_sr=96% cum_sr=97%]
|
||||||
|
... [9 sheep | 12,858,632 steps | ret(last 50)=+47.81 win_sr=96% cum_sr=97%]
|
||||||
|
... [9 sheep | 12,958,632 steps | ret(last 50)=+44.92 win_sr=90% cum_sr=96%]
|
||||||
|
... [9 sheep | 13,058,632 steps | ret(last 50)=+47.40 win_sr=90% cum_sr=96%]
|
||||||
|
... [9 sheep | 13,158,632 steps | ret(last 50)=+47.16 win_sr=92% cum_sr=95%]
|
||||||
|
... [9 sheep | 13,258,632 steps | ret(last 50)=+45.55 win_sr=98% cum_sr=96%]
|
||||||
|
... [9 sheep | 13,358,632 steps | ret(last 50)=+46.87 win_sr=96% cum_sr=96%]
|
||||||
|
... [9 sheep | 13,458,632 steps | ret(last 50)=+47.69 win_sr=98% cum_sr=96%]
|
||||||
|
... [9 sheep | 13,558,632 steps | ret(last 50)=+45.17 win_sr=94% cum_sr=96%]
|
||||||
|
[Stage n_sheep=9] evaluating 30 eps
|
||||||
|
[Stage n_sheep=9] sr=90% mean_len=1628 mean_min_pen=3.2m mean_act=1.38
|
||||||
|
failure modes: SUCCESS=27 COMPACT_CANT_DRIVE=3
|
||||||
|
reward/step: progress=+0.1802 alignment=+0.0204 compact=+0.0000 wall_touch=+0.0000 pen_bonus=+0.0514 step_cost=-0.0200 complete=+0.0553
|
||||||
|
|
||||||
|
[Stage n_sheep=10] training 1,500,000 steps
|
||||||
|
... [10 sheep | 13,565,960 steps | ret(last 0)=+nan win_sr=nan% cum_sr=nan%]
|
||||||
|
... [10 sheep | 13,665,960 steps | ret(last 50)=+49.00 win_sr=82% cum_sr=82%]
|
||||||
|
... [10 sheep | 13,765,960 steps | ret(last 50)=+48.55 win_sr=86% cum_sr=84%]
|
||||||
|
... [10 sheep | 13,865,960 steps | ret(last 50)=+46.53 win_sr=80% cum_sr=83%]
|
||||||
|
... [10 sheep | 13,965,960 steps | ret(last 50)=+44.70 win_sr=82% cum_sr=83%]
|
||||||
|
... [10 sheep | 14,065,960 steps | ret(last 50)=+52.57 win_sr=92% cum_sr=85%]
|
||||||
|
... [10 sheep | 14,165,960 steps | ret(last 50)=+50.20 win_sr=82% cum_sr=85%]
|
||||||
|
... [10 sheep | 14,265,960 steps | ret(last 50)=+50.34 win_sr=90% cum_sr=85%]
|
||||||
|
... [10 sheep | 14,365,960 steps | ret(last 50)=+50.24 win_sr=90% cum_sr=86%]
|
||||||
|
... [10 sheep | 14,465,960 steps | ret(last 50)=+48.40 win_sr=86% cum_sr=86%]
|
||||||
|
... [10 sheep | 14,565,960 steps | ret(last 50)=+48.74 win_sr=88% cum_sr=87%]
|
||||||
|
... [10 sheep | 14,665,960 steps | ret(last 50)=+48.46 win_sr=80% cum_sr=86%]
|
||||||
|
... [10 sheep | 14,765,960 steps | ret(last 50)=+51.46 win_sr=70% cum_sr=85%]
|
||||||
|
... [10 sheep | 14,865,960 steps | ret(last 50)=+49.28 win_sr=92% cum_sr=85%]
|
||||||
|
... [10 sheep | 14,965,960 steps | ret(last 50)=+51.12 win_sr=88% cum_sr=86%]
|
||||||
|
... [10 sheep | 15,065,960 steps | ret(last 50)=+52.03 win_sr=84% cum_sr=85%]
|
||||||
|
[Stage n_sheep=10] evaluating 30 eps
|
||||||
|
[Stage n_sheep=10] sr=93% mean_len=1870 mean_min_pen=3.1m mean_act=1.38
|
||||||
|
failure modes: SUCCESS=28 COMPACT_CANT_DRIVE=2
|
||||||
|
reward/step: progress=+0.1727 alignment=+0.0219 compact=+0.0000 wall_touch=+0.0000 pen_bonus=+0.0522 step_cost=-0.0200 complete=+0.0499
|
||||||
|
|
||||||
|
======================================================================
|
||||||
|
TRAINING SUMMARY
|
||||||
|
======================================================================
|
||||||
|
n_sheep=1 sr=100% len= 234 min_pen= 3.7m act=0.41
|
||||||
|
n_sheep=2 sr= 87% len= 1064 min_pen= 4.1m act=0.59
|
||||||
|
n_sheep=3 sr=100% len= 769 min_pen= 3.5m act=0.72
|
||||||
|
n_sheep=4 sr=100% len= 750 min_pen= 3.5m act=1.23
|
||||||
|
n_sheep=5 sr= 97% len= 921 min_pen= 3.2m act=1.33
|
||||||
|
n_sheep=6 sr= 97% len= 1193 min_pen= 3.4m act=1.36
|
||||||
|
n_sheep=7 sr=100% len= 1209 min_pen= 3.2m act=1.37
|
||||||
|
n_sheep=8 sr=100% len= 1492 min_pen= 3.2m act=1.38
|
||||||
|
n_sheep=9 sr= 90% len= 1628 min_pen= 3.2m act=1.38
|
||||||
|
n_sheep=10 sr= 93% len= 1870 min_pen= 3.1m act=1.38
|
||||||
|
|
||||||
|
Total time: 92.0 min
|
||||||
|
Artefacts: runs/v2/
|
||||||
|
Plots: runs/v2/success_rate.png, runs/v2/eval/
|
||||||
@@ -0,0 +1,14 @@
|
|||||||
|
{
|
||||||
|
"W_PER_SHEEP": 2.0,
|
||||||
|
"W_ALIGN": 0.05,
|
||||||
|
"W_PEN_BONUS": 10.0,
|
||||||
|
"W_COMPLETE": 100.0,
|
||||||
|
"W_STEP_COST": 0.02,
|
||||||
|
"W_COMPACT": 0.0,
|
||||||
|
"W_WALL_TOUCH": 0.0,
|
||||||
|
"WALL_TOUCH_BUFFER": 0.4,
|
||||||
|
"ALIGN_SHAPE": "standoff",
|
||||||
|
"ALIGN_GATED": true,
|
||||||
|
"ENTRY_AWARE": true,
|
||||||
|
"ent_coef": 0.02
|
||||||
|
}
|
||||||
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|
|||||||
|
[
|
||||||
|
{
|
||||||
|
"sr": 1.0,
|
||||||
|
"mean_len": 234.0,
|
||||||
|
"mean_min_pen": 3.6668872674306234,
|
||||||
|
"mean_act": 0.4068990752695293,
|
||||||
|
"failure_modes": {
|
||||||
|
"SUCCESS": 30
|
||||||
|
},
|
||||||
|
"reward_per_step": {
|
||||||
|
"progress": 0.11183513424165568,
|
||||||
|
"alignment": 0.0002786317654047819,
|
||||||
|
"compact": 0.0,
|
||||||
|
"wall_touch": 0.0,
|
||||||
|
"pen_bonus": 0.042735042735042736,
|
||||||
|
"step_cost": -0.019999999999999716,
|
||||||
|
"complete": 0.42735042735042733
|
||||||
|
},
|
||||||
|
"n_sheep": 1
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"sr": 0.8666666666666667,
|
||||||
|
"mean_len": 1063.6666666666667,
|
||||||
|
"mean_min_pen": 4.120940693219503,
|
||||||
|
"mean_act": 0.5870139278816712,
|
||||||
|
"failure_modes": {
|
||||||
|
"SUCCESS": 26,
|
||||||
|
"COMPACT_CANT_DRIVE": 4
|
||||||
|
},
|
||||||
|
"reward_per_step": {
|
||||||
|
"progress": 0.05651345582855781,
|
||||||
|
"alignment": 0.007121706701510673,
|
||||||
|
"compact": 0.0,
|
||||||
|
"wall_touch": 0.0,
|
||||||
|
"pen_bonus": 0.01629583202757756,
|
||||||
|
"step_cost": -0.0199999999999909,
|
||||||
|
"complete": 0.08147916013788781
|
||||||
|
},
|
||||||
|
"n_sheep": 2
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"sr": 1.0,
|
||||||
|
"mean_len": 768.6,
|
||||||
|
"mean_min_pen": 3.4802104949951174,
|
||||||
|
"mean_act": 0.7173416881465967,
|
||||||
|
"failure_modes": {
|
||||||
|
"SUCCESS": 30
|
||||||
|
},
|
||||||
|
"reward_per_step": {
|
||||||
|
"progress": 0.11210350058336283,
|
||||||
|
"alignment": 0.007752684222105381,
|
||||||
|
"compact": 0.0,
|
||||||
|
"wall_touch": 0.0,
|
||||||
|
"pen_bonus": 0.039032006245121,
|
||||||
|
"step_cost": -0.019999999999994387,
|
||||||
|
"complete": 0.13010668748373666
|
||||||
|
},
|
||||||
|
"n_sheep": 3
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"sr": 1.0,
|
||||||
|
"mean_len": 749.8666666666667,
|
||||||
|
"mean_min_pen": 3.491257842381795,
|
||||||
|
"mean_act": 1.2302732761302806,
|
||||||
|
"failure_modes": {
|
||||||
|
"SUCCESS": 30
|
||||||
|
},
|
||||||
|
"reward_per_step": {
|
||||||
|
"progress": 0.15859288932254823,
|
||||||
|
"alignment": 0.011327628562653137,
|
||||||
|
"compact": 0.0,
|
||||||
|
"wall_touch": 0.0,
|
||||||
|
"pen_bonus": 0.05334281650071124,
|
||||||
|
"step_cost": -0.0199999999999947,
|
||||||
|
"complete": 0.13335704125177808
|
||||||
|
},
|
||||||
|
"n_sheep": 4
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"sr": 0.9666666666666667,
|
||||||
|
"mean_len": 920.5666666666667,
|
||||||
|
"mean_min_pen": 3.2368871172269187,
|
||||||
|
"mean_act": 1.329068384219205,
|
||||||
|
"failure_modes": {
|
||||||
|
"SUCCESS": 29,
|
||||||
|
"PARTIAL_3of5": 1
|
||||||
|
},
|
||||||
|
"reward_per_step": {
|
||||||
|
"progress": 0.15654392868672135,
|
||||||
|
"alignment": 0.013497823599666012,
|
||||||
|
"compact": 0.0,
|
||||||
|
"wall_touch": 0.0,
|
||||||
|
"pen_bonus": 0.05359017996161784,
|
||||||
|
"step_cost": -0.019999999999992312,
|
||||||
|
"complete": 0.10500778505992686
|
||||||
|
},
|
||||||
|
"n_sheep": 5
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"sr": 0.9666666666666667,
|
||||||
|
"mean_len": 1193.2333333333333,
|
||||||
|
"mean_min_pen": 3.4217512369155885,
|
||||||
|
"mean_act": 1.3575613093489967,
|
||||||
|
"failure_modes": {
|
||||||
|
"COMPACT_CANT_DRIVE": 1,
|
||||||
|
"SUCCESS": 29
|
||||||
|
},
|
||||||
|
"reward_per_step": {
|
||||||
|
"progress": 0.15969395095863717,
|
||||||
|
"alignment": 0.017340700156353795,
|
||||||
|
"compact": 0.0,
|
||||||
|
"wall_touch": 0.0,
|
||||||
|
"pen_bonus": 0.049166131240048046,
|
||||||
|
"step_cost": -0.01999999999998991,
|
||||||
|
"complete": 0.08101237533871554
|
||||||
|
},
|
||||||
|
"n_sheep": 6
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"sr": 1.0,
|
||||||
|
"mean_len": 1209.4666666666667,
|
||||||
|
"mean_min_pen": 3.2339003403981526,
|
||||||
|
"mean_act": 1.3714931576761524,
|
||||||
|
"failure_modes": {
|
||||||
|
"SUCCESS": 30
|
||||||
|
},
|
||||||
|
"reward_per_step": {
|
||||||
|
"progress": 0.17738547200352864,
|
||||||
|
"alignment": 0.017914342656107935,
|
||||||
|
"compact": 0.0,
|
||||||
|
"wall_touch": 0.0,
|
||||||
|
"pen_bonus": 0.057876750082681075,
|
||||||
|
"step_cost": -0.019999999999989804,
|
||||||
|
"complete": 0.08268107154668725
|
||||||
|
},
|
||||||
|
"n_sheep": 7
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"sr": 1.0,
|
||||||
|
"mean_len": 1491.7666666666667,
|
||||||
|
"mean_min_pen": 3.216744065284729,
|
||||||
|
"mean_act": 1.3783802580111435,
|
||||||
|
"failure_modes": {
|
||||||
|
"SUCCESS": 30
|
||||||
|
},
|
||||||
|
"reward_per_step": {
|
||||||
|
"progress": 0.19162546125035912,
|
||||||
|
"alignment": 0.018971863842493202,
|
||||||
|
"compact": 0.0,
|
||||||
|
"wall_touch": 0.0,
|
||||||
|
"pen_bonus": 0.05362768976381472,
|
||||||
|
"step_cost": -0.01999999999998829,
|
||||||
|
"complete": 0.06703461220476839
|
||||||
|
},
|
||||||
|
"n_sheep": 8
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"sr": 0.9,
|
||||||
|
"mean_len": 1627.5666666666666,
|
||||||
|
"mean_min_pen": 3.23857311407725,
|
||||||
|
"mean_act": 1.3832202011732966,
|
||||||
|
"failure_modes": {
|
||||||
|
"SUCCESS": 27,
|
||||||
|
"COMPACT_CANT_DRIVE": 3
|
||||||
|
},
|
||||||
|
"reward_per_step": {
|
||||||
|
"progress": 0.18015228593205654,
|
||||||
|
"alignment": 0.020407598899987247,
|
||||||
|
"compact": 0.0,
|
||||||
|
"wall_touch": 0.0,
|
||||||
|
"pen_bonus": 0.05140598439388044,
|
||||||
|
"step_cost": -0.01999999999998775,
|
||||||
|
"complete": 0.055297274049194094
|
||||||
|
},
|
||||||
|
"n_sheep": 9
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"sr": 0.9333333333333333,
|
||||||
|
"mean_len": 1869.9666666666667,
|
||||||
|
"mean_min_pen": 3.1344878753026326,
|
||||||
|
"mean_act": 1.3841143385300063,
|
||||||
|
"failure_modes": {
|
||||||
|
"SUCCESS": 28,
|
||||||
|
"COMPACT_CANT_DRIVE": 2
|
||||||
|
},
|
||||||
|
"reward_per_step": {
|
||||||
|
"progress": 0.17267533684098152,
|
||||||
|
"alignment": 0.021850885374692264,
|
||||||
|
"compact": 0.0,
|
||||||
|
"wall_touch": 0.0,
|
||||||
|
"pen_bonus": 0.05222909499278062,
|
||||||
|
"step_cost": -0.019999999999986983,
|
||||||
|
"complete": 0.04991176313303267
|
||||||
|
},
|
||||||
|
"n_sheep": 10
|
||||||
|
}
|
||||||
|
]
|
||||||
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|
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Reference in New Issue
Block a user