Offline Reinforcement Learning on cube-double-play OGBench 5 tasks v0
69Average Success RateValue Flows
Evaluation Results
| Method | Links | |
|---|---|---|
| Value FlowsPolicy Type=Flow Policies2025.10 | 69 | |
| CODACPolicy Type=Flow Policies2025.10 | 61 | |
| MACModel Paradigm=Model-Based2025.12 | 53 | |
| IQNPolicy Type=Flow Policies2025.10 | 42 | |
| FQLModel Paradigm=Model-Free2025.12 | 29 | |
| FQLPolicy Type=Flow Policies2025.10 | 29 | |
| IDQLModel Paradigm=Model-Free2025.12 | 15 | |
| FBRACPolicy Type=Flow Policies2025.10 | 15 | |
| IFQLPolicy Type=Flow Policies2025.10 | 14 | |
| ReBRACModel Paradigm=Model-Free2025.12 | 12 | |
| ReBRACPolicy Type=Gaussian Policies2025.10 | 12 | |
| IQLModel Paradigm=Model-Free2025.12 | 7 | |
| IQLPolicy Type=Gaussian Policies2025.10 | 6 | |
| FMPCModel Paradigm=Model-Based2025.12 | 3 | |
| BCPolicy Type=Gaussian Policies2025.10 | 2 | |
| C51Policy Type=Flow Policies2025.10 | 2 | |
| MOPOModel Paradigm=Model-Based2025.12 | 1 | |
| MOBILEModel Paradigm=Model-Based2025.12 | 1 | |
| LEQModel Paradigm=Model-Based2025.12 | 0 |