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Benchmarks
Multi-Task Reinforcement Learning on Meta-World MT10 v1 (train test)
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91
Average Success
Nash-MTL
47.32
58.66
70
81.34
Feb 2, 2022
Average Success
Updated 4d ago
Evaluation Results
Method
Method
Links
Average Success
Nash-MTL
Base RL algorithm=SAC,...
2022.02
91
STL SAC
Base RL algorithm=SAC,...
2022.02
90
CARE
Base RL algorithm=SAC,...
2022.02
84
CAGrad
Base RL algorithm=SAC,...
2022.02
83
SM
Base RL algorithm=SAC,...
2022.02
73
PCGrad
Base RL algorithm=SAC,...
2022.02
72
MH SAC
Base RL algorithm=SAC,...
2022.02
61
MTL SAC + TE
Base RL algorithm=SAC,...
2022.02
54
MTL SAC
Base RL algorithm=SAC,...
2022.02
49
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