Research map / Multi-agent systems and games / Human-AI and goal-conditioned
Verification of RNN-Based Neural Agent-Environment Systems
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use Input on Demand unrolling instead of Input on Start (ablation) | RNSVerify · Pendulum-v0, X^8(theta_f > -pi/10) | MILP solve time: 693.2s -> 0.689s | Helped |
| use Input on Demand unrolling instead of Input on Start (ablation) | RNSVerify · Pendulum-v0, X^7(theta_f > -pi/70) | MILP solve time: 207.8s -> 0.478s | Helped |
| use Input on Demand unrolling instead of Input on Start (ablation) | RNSVerify · Pendulum-v0 n=8 | MILP variables: 11130 -> 4970 | Helped |
| use Input on Demand unrolling instead of Input on Start (ablation) | RNSVerify · Pendulum-v0 n=8 | MILP constraints: 13881 -> 6181 | Helped |
| use Input on Demand unrolling instead of Input on Start (ablation) | RNSVerify · Pendulum-v0, n=1 with small eps | MILP solve time: 0.014s vs 0.014s | No clear effect |
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