Research map / Reinforcement learning / Policy gradient and actor-critic
Quantum Policy Gradient Algorithm with Optimized Action Decoding
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use optimal globality (G=n) parity-based post-processing instead of low-globality (G=1.0) post-processing (ablation) | RAW-VQC policy · CartPole-v0 | convergence speed / collected reward | Helped |
| Increase globality of post-processing function (higher G correlates with faster convergence) (ablation) | RAW-VQC policy · CartPole-v0 | convergence speed to reward 200 | Helped |
| Use RAW-VQC with globality-optimal post-processing instead of SOFTMAX-VQC | RAW-VQC policy · CartPole-v0 | convergence speed | Helped |
| Replace CZ gates with CX gates and use batch size 50 for hardware training | RAW-VQC on ibmq_manila · ContextualBandits 8-state hardware | hardware training performance | No clear effect |
| Add matrix-free measurement error mitigation to hardware training (ablation) | RAW-VQC on ibmq_manila · ContextualBandits 8-state hardware | training performance | Helped |
| Increase circuit depth of RAW-VQC with low globality (ablation) | RAW-VQC with G=1.0 · CartPole-v0 | learning optimal behavior | Mixed |
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