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ReBRAC finetune #9

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Dec 6, 2023
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ReBRAC finetune #9

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7075f8d
ReBRAC finetune
Aug 17, 2023
b969c3f
Fix
Aug 17, 2023
ea2fa11
linter
Aug 17, 2023
61ebf4a
fix linter
Aug 17, 2023
94aa863
Fix
Aug 19, 2023
73e1747
Fix configs
Aug 19, 2023
125d77c
Fix umaze config
Aug 20, 2023
72bd251
Add scoring
Sep 15, 2023
d60ad7b
Change arrows in README
Sep 15, 2023
d44a995
Update get_finetune_scores.py
DT6A Sep 15, 2023
7f002ec
Update rebrac.py
DT6A Sep 15, 2023
0a185d3
Update get_finetune_urls.py
DT6A Sep 15, 2023
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Update get_finetune_scores.py
DT6A Sep 15, 2023
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Update rebrac.py
DT6A Sep 15, 2023
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Merge branch 'main' into rebrac-finetune
DT6A Dec 6, 2023
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Update README.md
DT6A Dec 6, 2023
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58 changes: 29 additions & 29 deletions README.md
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Expand Up @@ -47,6 +47,7 @@ docker run --gpus=all -it --rm --name <container_name> <image_name>
| ✅ [Conservative Q-Learning for Offline Reinforcement Learning <br>(CQL)](https://arxiv.org/abs/2006.04779) | [`offline/cql.py`](algorithms/offline/cql.py) <br /> [`finetune/cql.py`](algorithms/finetune/cql.py) | [`Offline`](https://wandb.ai/tlab/CORL/reports/-Offline-CQL--Vmlldzo1MzM4MjY3) <br /> <br /> [`Offline-to-online`](https://wandb.ai/tlab/CORL/reports/-Offline-to-Online-CQL--Vmlldzo0NTQ3NTMz)
| ✅ [Accelerating Online Reinforcement Learning with Offline Datasets <br>(AWAC)](https://arxiv.org/abs/2006.09359) | [`offline/awac.py`](algorithms/offline/awac.py) <br /> [`finetune/awac.py`](algorithms/finetune/awac.py) | [`Offline`](https://wandb.ai/tlab/CORL/reports/-Offline-AWAC--Vmlldzo1MzM4MTEy) <br /> <br /> [`Offline-to-online`](https://wandb.ai/tlab/CORL/reports/-Offline-to-Online-AWAC--VmlldzozODAyNzQz)
| ✅ [Offline Reinforcement Learning with Implicit Q-Learning <br>(IQL)](https://arxiv.org/abs/2110.06169) | [`offline/iql.py`](algorithms/offline/iql.py) <br /> [`finetune/iql.py`](algorithms/finetune/iql.py) | [`Offline`](https://wandb.ai/tlab/CORL/reports/-Offline-IQL--Vmlldzo1MzM4MzQz) <br /> <br /> [`Offline-to-online`](https://wandb.ai/tlab/CORL/reports/-Offline-to-Online-IQL--VmlldzozNzE1MTEy)
| ✅ [Revisiting the Minimalist Approach to Offline Reinforcement Learning <br>(ReBRAC)](https://arxiv.org/abs/2305.09836) | [`offline/rebrac.py`](algorithms/offline/rebrac.py) <br /> [`finetune/rebrac.py`](algorithms/finetune/rebrac.py) | [`Offline`](https://wandb.ai/tlab/CORL/reports/-Offline-ReBRAC--Vmlldzo0ODkzOTQ2) <br /> <br /> [`Offline-to-online`](https://wandb.ai/tlab/CORL/reports/-Offline-to-Online-ReBRAC--Vmlldzo1NDAyNjE5)
| **Offline-to-Online only** | |
| ✅ [Supported Policy Optimization for Offline Reinforcement Learning <br>(SPOT)](https://arxiv.org/abs/2202.06239) | [`finetune/spot.py`](algorithms/finetune/spot.py) | [`Offline-to-online`](https://wandb.ai/tlab/CORL/reports/-Offline-to-Online-SPOT--VmlldzozODk5MTgx)
| ✅ [Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning <br>(Cal-QL)](https://arxiv.org/abs/2303.05479) | [`finetune/cal_ql.py`](algorithms/finetune/cal_ql.py) | [`Offline-to-online`](https://wandb.ai/tlab/CORL/reports/-Offline-to-Online-Cal-QL--Vmlldzo0NTQ3NDk5)
Expand All @@ -57,7 +58,6 @@ docker run --gpus=all -it --rm --name <container_name> <image_name>
| ✅ [Decision Transformer: Reinforcement Learning via Sequence Modeling <br>(DT)](https://arxiv.org/abs/2106.01345) | [`offline/dt.py`](algorithms/offline/dt.py) | [`Offline`](https://wandb.ai/tlab/CORL/reports/-Offline-Decision-Transformer--Vmlldzo1MzM3OTkx)
| ✅ [Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble <br>(SAC-N)](https://arxiv.org/abs/2110.01548) | [`offline/sac_n.py`](algorithms/offline/sac_n.py) | [`Offline`](https://wandb.ai/tlab/CORL/reports/-Offline-SAC-N--VmlldzoyNzA1NTY1)
| ✅ [Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble <br>(EDAC)](https://arxiv.org/abs/2110.01548) | [`offline/edac.py`](algorithms/offline/edac.py) | [`Offline`](https://wandb.ai/tlab/CORL/reports/-Offline-EDAC--VmlldzoyNzA5ODUw)
| ✅ [Revisiting the Minimalist Approach to Offline Reinforcement Learning <br>(ReBRAC)](https://arxiv.org/abs/2305.09836) | [`offline/rebrac.py`](algorithms/offline/rebrac.py) | [`Offline`](https://wandb.ai/tlab/CORL/reports/-Offline-ReBRAC--Vmlldzo0ODkzOTQ2)
| ✅ [Q-Ensemble for Offline RL: Don't Scale the Ensemble, Scale the Batch Size <br>(LB-SAC)](https://arxiv.org/abs/2211.11092) | [`offline/lb_sac.py`](algorithms/offline/lb_sac.py) | [`Offline Gym-MuJoCo`](https://wandb.ai/tlab/CORL/reports/LB-SAC-D4RL-Results--VmlldzozNjIxMDY1)


Expand Down Expand Up @@ -179,42 +179,42 @@ You can check the links above for learning curves and details. Here, we report r

### Offline-to-Online
#### Scores
| **Task-Name** |AWAC|CQL|IQL|SPOT|Cal-QL|
|---------------------------|------------|--------|--------|-----|-----|
|antmaze-umaze-v2|52.75 ± 8.67 → 98.75 ± 1.09|94.00 ± 1.58 → 99.50 ± 0.87|77.00 ± 0.71 → 96.50 ± 1.12|91.00 ± 2.55 → 99.50 ± 0.50|76.75 ± 7.53 → 99.75 ± 0.43|
|antmaze-umaze-diverse-v2|56.00 ± 2.74 → 0.00 ± 0.00|9.50 ± 9.91 → 99.00 ± 1.22|59.50 ± 9.55 → 63.75 ± 25.02|36.25 ± 2.17 → 95.00 ± 3.67|32.00 ± 27.79 → 98.50 ± 1.12|
|antmaze-medium-play-v2|0.00 ± 0.00 → 0.00 ± 0.00|59.00 ± 11.18 → 97.75 ± 1.30|71.75 ± 2.95 → 89.75 ± 1.09|67.25 ± 10.47 → 97.25 ± 1.30|71.75 ± 3.27 → 98.75 ± 1.64|
|antmaze-medium-diverse-v2|0.00 ± 0.00 → 0.00 ± 0.00|63.50 ± 6.84 → 97.25 ± 1.92|64.25 ± 1.92 → 92.25 ± 2.86|73.75 ± 7.29 → 94.50 ± 1.66|62.00 ± 4.30 → 98.25 ± 1.48|
|antmaze-large-play-v2|0.00 ± 0.00 → 0.00 ± 0.00|28.75 ± 7.76 → 88.25 ± 2.28|38.50 ± 8.73 → 64.50 ± 17.04|31.50 ± 12.58 → 87.00 ± 3.24|31.75 ± 8.87 → 97.25 ± 1.79|
|antmaze-large-diverse-v2|0.00 ± 0.00 → 0.00 ± 0.00|35.50 ± 3.64 → 91.75 ± 3.96|26.75 ± 3.77 → 64.25 ± 4.15|17.50 ± 7.26 → 81.00 ± 14.14|44.00 ± 8.69 → 91.50 ± 3.91|
| **Task-Name** |AWAC|CQL|IQL|SPOT|Cal-QL|ReBRAC|
|---------------------------|------------|--------|--------|-----|-----|-----|
|antmaze-umaze-v2|52.75 ± 8.67 → 98.75 ± 1.09|94.00 ± 1.58 → 99.50 ± 0.87|77.00 ± 0.71 → 96.50 ± 1.12|91.00 ± 2.55 → 99.50 ± 0.50|76.75 ± 7.53 → 99.75 ± 0.43|98.00 ± 1.58 → 74.75 ± 42.59|
|antmaze-umaze-diverse-v2|56.00 ± 2.74 → 0.00 ± 0.00|9.50 ± 9.91 → 99.00 ± 1.22|59.50 ± 9.55 → 63.75 ± 25.02|36.25 ± 2.17 → 95.00 ± 3.67|32.00 ± 27.79 → 98.50 ± 1.12|73.75 ± 13.27 → 98.00 ± 2.92|
|antmaze-medium-play-v2|0.00 ± 0.00 → 0.00 ± 0.00|59.00 ± 11.18 → 97.75 ± 1.30|71.75 ± 2.95 → 89.75 ± 1.09|67.25 ± 10.47 → 97.25 ± 1.30|71.75 ± 3.27 → 98.75 ± 1.64|87.50 ± 3.77 → 98.00 ± 1.58|
|antmaze-medium-diverse-v2|0.00 ± 0.00 → 0.00 ± 0.00|63.50 ± 6.84 → 97.25 ± 1.92|64.25 ± 1.92 → 92.25 ± 2.86|73.75 ± 7.29 → 94.50 ± 1.66|62.00 ± 4.30 → 98.25 ± 1.48|85.25 ± 2.17 → 98.75 ± 0.43|
|antmaze-large-play-v2|0.00 ± 0.00 → 0.00 ± 0.00|28.75 ± 7.76 → 88.25 ± 2.28|38.50 ± 8.73 → 64.50 ± 17.04|31.50 ± 12.58 → 87.00 ± 3.24|31.75 ± 8.87 → 97.25 ± 1.79|68.50 ± 6.18 → 31.50 ± 33.56|
|antmaze-large-diverse-v2|0.00 ± 0.00 → 0.00 ± 0.00|35.50 ± 3.64 → 91.75 ± 3.96|26.75 ± 3.77 → 64.25 ± 4.15|17.50 ± 7.26 → 81.00 ± 14.14|44.00 ± 8.69 → 91.50 ± 3.91|67.00 ± 10.61 → 72.25 ± 41.73|
| | | | | | | | | | |
| **antmaze average** |18.12 → 16.46|48.38 → 95.58|56.29 → 78.50|52.88 → 92.38|53.04 → 97.33|
| **antmaze average** |18.12 → 16.46|48.38 → 95.58|56.29 → 78.50|52.88 → 92.38|53.04 → 97.33|80.00 → 78.88|
| | | | | | | | | | |
|pen-cloned-v1|88.66 ± 15.10 → 86.82 ± 11.12|-2.76 ± 0.08 → -1.28 ± 2.16|84.19 ± 3.96 → 102.02 ± 20.75|6.19 ± 5.21 → 43.63 ± 20.09|-2.66 ± 0.04 → -2.68 ± 0.12|
|door-cloned-v1|0.93 ± 1.66 → 0.01 ± 0.00|-0.33 ± 0.01 → -0.33 ± 0.01|1.19 ± 0.93 → 20.34 ± 9.32|-0.21 ± 0.14 → 0.02 ± 0.31|-0.33 ± 0.01 → -0.33 ± 0.01|
|hammer-cloned-v1|1.80 ± 3.01 → 0.24 ± 0.04|0.56 ± 0.55 → 2.85 ± 4.81|1.35 ± 0.32 → 57.27 ± 28.49|3.97 ± 6.39 → 3.73 ± 4.99|0.25 ± 0.04 → 0.17 ± 0.17|
|relocate-cloned-v1|-0.04 ± 0.04 → -0.04 ± 0.01|-0.33 ± 0.01 → -0.33 ± 0.01|0.04 ± 0.04 → 0.32 ± 0.38|-0.24 ± 0.01 → -0.15 ± 0.05|-0.31 ± 0.05 → -0.31 ± 0.04|
|pen-cloned-v1|88.66 ± 15.10 → 86.82 ± 11.12|-2.76 ± 0.08 → -1.28 ± 2.16|84.19 ± 3.96 → 102.02 ± 20.75|6.19 ± 5.21 → 43.63 ± 20.09|-2.66 ± 0.04 → -2.68 ± 0.12|74.04 ± 11.97 → 138.15 ± 3.22|
|door-cloned-v1|0.93 ± 1.66 → 0.01 ± 0.00|-0.33 ± 0.01 → -0.33 ± 0.01|1.19 ± 0.93 → 20.34 ± 9.32|-0.21 ± 0.14 → 0.02 ± 0.31|-0.33 ± 0.01 → -0.33 ± 0.01|0.07 ± 0.04 → 102.39 ± 8.27|
|hammer-cloned-v1|1.80 ± 3.01 → 0.24 ± 0.04|0.56 ± 0.55 → 2.85 ± 4.81|1.35 ± 0.32 → 57.27 ± 28.49|3.97 ± 6.39 → 3.73 ± 4.99|0.25 ± 0.04 → 0.17 ± 0.17|6.54 ± 3.35 → 124.65 ± 7.37|
|relocate-cloned-v1|-0.04 ± 0.04 → -0.04 ± 0.01|-0.33 ± 0.01 → -0.33 ± 0.01|0.04 ± 0.04 → 0.32 ± 0.38|-0.24 ± 0.01 → -0.15 ± 0.05|-0.31 ± 0.05 → -0.31 ± 0.04|0.70 ± 0.62 → 6.96 ± 4.59|
| | | | | | | | | | |
| **adroit average** |22.84 → 21.76|-0.72 → 0.22|21.69 → 44.99|2.43 → 11.81|-0.76 → -0.79|
| **adroit average** |22.84 → 21.76|-0.72 → 0.22|21.69 → 44.99|2.43 → 11.81|-0.76 → -0.79|20.33 → 93.04|

#### Regrets
| **Task-Name** |AWAC|CQL|IQL|SPOT|Cal-QL|
|---------------------------|------------|--------|--------|-----|-----|
|antmaze-umaze-v2|0.04 ± 0.01|0.02 ± 0.00|0.07 ± 0.00|0.02 ± 0.00|0.01 ± 0.00|
|antmaze-umaze-diverse-v2|0.88 ± 0.01|0.09 ± 0.01|0.43 ± 0.11|0.22 ± 0.07|0.05 ± 0.01|
|antmaze-medium-play-v2|1.00 ± 0.00|0.08 ± 0.01|0.09 ± 0.01|0.06 ± 0.00|0.04 ± 0.01|
|antmaze-medium-diverse-v2|1.00 ± 0.00|0.08 ± 0.00|0.10 ± 0.01|0.05 ± 0.01|0.04 ± 0.01|
|antmaze-large-play-v2|1.00 ± 0.00|0.21 ± 0.02|0.34 ± 0.05|0.29 ± 0.07|0.13 ± 0.02|
|antmaze-large-diverse-v2|1.00 ± 0.00|0.21 ± 0.03|0.41 ± 0.03|0.23 ± 0.08|0.13 ± 0.02|
| **Task-Name** |AWAC|CQL|IQL|SPOT|Cal-QL|ReBRAC|
|---------------------------|------------|--------|--------|-----|-----|-----|
|antmaze-umaze-v2|0.04 ± 0.01|0.02 ± 0.00|0.07 ± 0.00|0.02 ± 0.00|0.01 ± 0.00|0.11 ± 0.18|
|antmaze-umaze-diverse-v2|0.88 ± 0.01|0.09 ± 0.01|0.43 ± 0.11|0.22 ± 0.07|0.05 ± 0.01|0.04 ± 0.02|
|antmaze-medium-play-v2|1.00 ± 0.00|0.08 ± 0.01|0.09 ± 0.01|0.06 ± 0.00|0.04 ± 0.01|0.03 ± 0.01|
|antmaze-medium-diverse-v2|1.00 ± 0.00|0.08 ± 0.00|0.10 ± 0.01|0.05 ± 0.01|0.04 ± 0.01|0.03 ± 0.00|
|antmaze-large-play-v2|1.00 ± 0.00|0.21 ± 0.02|0.34 ± 0.05|0.29 ± 0.07|0.13 ± 0.02|0.14 ± 0.05|
|antmaze-large-diverse-v2|1.00 ± 0.00|0.21 ± 0.03|0.41 ± 0.03|0.23 ± 0.08|0.13 ± 0.02|0.29 ± 0.39|
| | | | | | | | | | |
| **antmaze average** |0.82|0.11|0.24|0.15|0.07|
| **antmaze average** |0.82|0.11|0.24|0.15|0.07|0.11|
| | | | | | | | | | |
|pen-cloned-v1|0.46 ± 0.02|0.97 ± 0.00|0.37 ± 0.01|0.58 ± 0.02|0.98 ± 0.01|
|door-cloned-v1|1.00 ± 0.00|1.00 ± 0.00|0.83 ± 0.03|0.99 ± 0.01|1.00 ± 0.00|
|hammer-cloned-v1|1.00 ± 0.00|1.00 ± 0.00|0.65 ± 0.10|0.98 ± 0.01|1.00 ± 0.00|
|relocate-cloned-v1|1.00 ± 0.00|1.00 ± 0.00|1.00 ± 0.00|1.00 ± 0.00|1.00 ± 0.00|
|pen-cloned-v1|0.46 ± 0.02|0.97 ± 0.00|0.37 ± 0.01|0.58 ± 0.02|0.98 ± 0.01|0.08 ± 0.01|
|door-cloned-v1|1.00 ± 0.00|1.00 ± 0.00|0.83 ± 0.03|0.99 ± 0.01|1.00 ± 0.00|0.19 ± 0.05|
|hammer-cloned-v1|1.00 ± 0.00|1.00 ± 0.00|0.65 ± 0.10|0.98 ± 0.01|1.00 ± 0.00|0.13 ± 0.03|
|relocate-cloned-v1|1.00 ± 0.00|1.00 ± 0.00|1.00 ± 0.00|1.00 ± 0.00|1.00 ± 0.00|0.90 ± 0.06|
| | | | | | | | | | |
| **adroit average** |0.86|0.99|0.71|0.89|0.99|
| **adroit average** |0.86|0.99|0.71|0.89|0.99|0.33|

## Citing CORL

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