Offline Reinforcement Learning on Walker2d
2,278.9Average ReturnUWMSG
Evaluation Results
| Method | Links | |
|---|---|---|
| UWMSGAttack Type=Random Dynamics2023.10 | 2,278.9 | |
| UWMSGAttack Type=Random Reward2023.10 | 2,189.7 | |
| MSGAttack Type=Random Dynamics2023.10 | 2,122.8 | |
| UWMSGAttack Type=Adversarial Reward2023.10 | 1,433.4 | |
| UWMSGAttack Type=Adversarial Dynamics2023.10 | 946 | |
| MSGAttack Type=Adversarial Reward2023.10 | 605.8 | |
| MSGAttack Type=Random Reward2023.10 | 539.1 | |
| MSGAttack Type=Adversarial Dynamics2023.10 | 506 | |
| EDACAttack Type=Adversarial Reward2023.10 | 61 | |
| SAC-NAttack Type=Adversarial Reward2023.10 | 9.5 | |
| SAC-NAttack Type=Random Dynamics2023.10 | -3.1 | |
| EDACAttack Type=Random Dynamics2023.10 | -3.6 | |
| EDACAttack Type=Random Reward2023.10 | -3.7 | |
| SAC-NAttack Type=Random Reward2023.10 | -3.8 | |
| EDACAttack Type=Adversarial Dynamics2023.10 | -4.8 | |
| SAC-NAttack Type=Adversarial Dynamics2023.10 | -5.1 |