CORL (Clean Offline Reinforcement Learning)

🧵 CORL is an Offline Reinforcement Learning library that provides high-quality and easy-to-follow single-file implementations of SOTA ORL algorithms. Each implementation is backed by a research-friendly codebase, allowing you to run or tune thousands of experiments. Heavily inspired by cleanrl for online RL, check them out too!

• 📜 Single-file implementation
• 📈 Benchmarked Implementation for N algorithms
• 🖼 Weights and Biases integration

Getting started

git clone https://github.com/tinkoff-ai/CORL.git && cd CORL
pip install -r requirements/requirements_dev.txt

# alternatively, you could use docker
docker build -t <image_name> .
docker run gpus=all -it --rm --name <container_name> <image_name>

Algorithms Implemented

Algorithm	Variants Implemented	Wandb Report
✅ Behavioral Cloning (BC)	any_percent_bc.py	Gym-MuJoCo, Maze2D
✅ Behavioral Cloning-10% (BC-10%)	any_percent_bc.py	Gym-MuJoCo, Maze2D
✅ Conservative Q-Learning for Offline Reinforcement Learning (CQL)	cql.py	Gym-MuJoCo, Maze2D
✅ Accelerating Online Reinforcement Learning with Offline Datasets (AWAC)	awac.py	Gym-MuJoCo, Maze2D
✅ Offline Reinforcement Learning with Implicit Q-Learning (IQL)	iql.py	Gym-MuJoCo, Maze2D
✅ A Minimalist Approach to Offline Reinforcement Learning (TD3+BC)	td3_bc.py	Gym-MuJoCo, Maze2D
✅ Decision Transformer: Reinforcement Learning via Sequence Modeling (DT)	dt.py	Gym-MuJoCo, Maze2D
✅ Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble (SAC-N)	sac_n.py	Gym-MuJoCo, Maze2D
✅ Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble (EDAC)	edac.py	Gym-MuJoCo, Maze2D

D4RL Benchmarks

For learning curves and all the details, you can check the links above. Here, we report reproduced final and best scores. Note that thay differ by a big margin, and some papers may use different approaches not making it always explicit which one reporting methodology they chose.

Last Scores

Gym-MuJoCo

Task-Name	BC	BC-10%	TD3 + BC	CQL	IQL	AWAC	SAC-N	EDAC	DT
halfcheetah-medium-v2	42.40±0.21	42.29±0.40	48.10±0.21	46.64±0.24	48.31±0.11	49.78±0.42	68.20±1.48	67.70±1.20	41.44±0.39
halfcheetah-medium-expert-v2	55.95±8.49	91.45±2.57	90.78±6.98	87.10±11.41	94.55±0.21	95.56±1.09	98.96±10.74	104.76±0.74	84.39±4.27
halfcheetah-medium-replay-v2	35.66±2.68	29.65±2.11	44.84±0.68	44.67±0.28	43.53±0.43	44.95±0.86	60.70±1.17	62.06±1.27	27.50±5.49
hopper-medium-v2	53.51±2.03	51.16±12.98	60.37±4.03	56.88±4.46	62.75±6.02	65.06±5.97	40.82±11.44	101.70±0.32	48.41±6.11
hopper-medium-expert-v2	52.30±4.63	105.17±7.12	101.17±10.48	86.95±17.45	106.24±6.09	105.38±7.31	101.31±13.43	105.19±11.64	83.20±26.68
hopper-medium-replay-v2	29.81±2.39	23.89±11.61	64.42±24.84	84.21±18.27	84.57±13.49	98.15±2.85	100.33±0.90	99.66±0.94	42.83±22.92
walker2d-medium-v2	63.23±18.76	58.56±4.14	82.71±5.51	80.58±3.80	84.03±5.42	69.39±31.97	87.47±0.76	93.36±1.60	69.15±6.76
walker2d-medium-expert-v2	98.96±18.45	108.45±0.30	110.03±0.41	110.23±0.48	111.68±0.56	111.65±1.74	114.93±0.48	114.75±0.86	92.64±3.35
walker2d-medium-replay-v2	21.80±11.72	41.99±17.77	85.62±4.63	82.16±2.32	82.55±8.00	80.43±3.95	78.99±0.58	87.10±3.21	16.93±19.57
locomotion average	50.40	61.40	76.45	75.49	79.80	80.04	83.52	92.92	56.28

Maze2d

Task-Name	BC	BC-10%	TD3 + BC	CQL	IQL	AWAC	SAC-N	EDAC	DT
maze2d-umaze-v1	0.36±10.03	-2.98±6.68	29.41±14.22	-6.97±17.41	37.69±1.99	60.09±19.09	131.08±19.36	90.74±6.51	-14.55±0.15
maze2d-medium-v1	0.79±3.76	2.04±3.52	59.45±41.86	2.77±7.24	35.45±0.98	79.42±50.93	88.55±21.68	62.36±9.76	-0.38±7.26
maze2d-large-v1	2.26±5.07	3.14±4.77	97.10±29.34	1.29±7.11	49.64±22.02	217.44±4.93	205.13±1.33	108.17±25.02	-0.45±1.51
maze2d average	1.13	0.74	61.99	-0.97	40.92	118.98	141.59	87.09	-5.13

Antmaze

Task-Name	BC	BC-10%	TD3 + BC	CQL	IQL	AWAC	SAC-N	EDAC	DT
antmaze-umaze-v0	51.50±8.81	0.00±0.00	93.25±1.50	67.00±6.24	74.50±11.03	63.50±9.33	TBD±TBD	TBD±TBD	52.75±11.47
antmaze-medium-play-v0	0.00±0.00	0.00±0.00	0.00±0.00	0.00±0.00	68.00±12.77	0.00±0.00	TBD±TBD	TBD±TBD	0.00±0.00
antmaze-large-play-v0	0.00±0.00	0.00±0.00	0.00±0.00	0.00±0.00	45.00±11.53	0.00±0.00	TBD±TBD	TBD±TBD	0.00±0.00
antmaze average	17.17	0.00	31.08	22.33	62.50	21.17	TBD	TBD	17.58

Best Scores

Gym-MuJoCo

Task-Name	BC	BC-10%	TD3 + BC	CQL	IQL	AWAC	SAC-N	EDAC	DT
halfcheetah-medium-v2	43.60±0.16	43.74±0.18	48.93±0.13	47.26±0.23	48.77±0.06	50.79±0.19	72.21±0.35	69.72±1.06	42.63±0.09
halfcheetah-medium-expert-v2	79.69±3.58	93.98±0.18	96.59±1.01	95.82±0.31	95.83±0.38	96.85±0.32	111.73±0.55	110.62±1.20	87.34±0.65
halfcheetah-medium-replay-v2	40.52±0.22	41.45±0.10	45.84±0.30	45.97±0.32	45.06±0.16	46.56±0.27	67.29±0.39	66.55±1.21	32.20±2.50
hopper-medium-v2	69.04±3.35	66.91±2.30	70.44±1.37	69.09±0.85	80.74±1.27	99.25±0.87	101.79±0.23	103.26±0.16	61.95±4.63
hopper-medium-expert-v2	90.63±12.68	113.05±0.17	113.22±0.50	111.01±1.93	111.79±0.47	113.25±0.50	111.24±0.17	111.80±0.13	107.01±3.28
hopper-medium-replay-v2	68.88±11.93	53.82±8.10	98.12±1.34	102.10±0.41	102.33±0.44	101.68±0.38	103.83±0.61	103.28±0.57	59.65±13.50
walker2d-medium-v2	80.64±1.06	80.46±1.41	86.91±0.32	84.76±0.15	87.99±0.83	85.98±4.43	90.17±0.63	95.78±1.23	75.54±0.53
walker2d-medium-expert-v2	109.95±0.72	109.57±0.33	112.21±0.07	111.70±0.28	113.19±0.33	113.30±2.51	116.93±0.49	116.52±0.86	96.30±1.18
walker2d-medium-replay-v2	48.41±8.78	71.54±1.16	91.17±0.83	88.02±1.18	91.85±2.26	86.79±0.96	85.18±1.89	89.69±1.60	67.23±6.73
locomotion average	70.15	74.95	84.83	83.97	86.40	88.27	95.60	96.36	69.98

Maze2d

Task-Name	BC	BC-10%	TD3 + BC	CQL	IQL	AWAC	SAC-N	EDAC	DT
maze2d-umaze-v1	16.09±1.00	16.85±0.60	99.33±18.66	18.82±0.63	44.04±3.02	137.96±12.50	151.28±8.14	144.30±5.60	-14.19±0.56
maze2d-medium-v1	19.16±1.44	24.81±4.09	150.93±4.50	17.96±5.24	92.25±40.74	152.11±23.00	90.04±20.74	150.82±2.76	45.13±6.25
maze2d-large-v1	20.75±7.69	35.66±6.40	197.64±6.07	12.27±5.34	138.70±44.70	227.79±1.99	207.10±1.46	179.90±2.41	3.94±2.24
maze2d average	18.67	25.77	149.30	16.35	91.66	172.62	149.47	158.34	11.63

Antmaze

Task-Name	BC	BC-10%	TD3 + BC	CQL	IQL	AWAC	SAC-N	EDAC	DT
antmaze-umaze-v0	71.25±9.07	0.00±0.00	97.75±1.50	78.33±4.73	87.00±2.94	74.75±8.77	TBD±TBD	TBD±TBD	65.50±8.96
antmaze-medium-play-v0	4.75±2.22	0.00±0.00	6.00±2.00	2.00±1.41	85.33±2.08	14.00±11.80	TBD±TBD	TBD±TBD	1.00±2.00
antmaze-large-play-v0	0.75±0.50	0.00±0.00	0.33±0.58	0.00±0.00	56.00±4.00	0.00±0.00	TBD±TBD	TBD±TBD	0.00±0.00
antmaze average	25.58	0.00	34.69	26.78	76.11	29.58	TBD	TBD	22.17

Citing CORL

If you use CORL in your work, please use the following bibtex

@misc{corl2022,
  author={Tarasov, Denis and Nikulin, Alexander and Akimov, Dmitriy and Kurenkov, Vladislav and Sergey Kolesnikov},
  title={CORL: Research-oriented Deep Offline Reinforcement Learning Library},
  year={2022},
  url={https://github.com/tinkoff-ai/CORL},
}