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OmniSafe's Safety-Gymnasium Benchmark for On-Policy Algorithms

The OmniSafe Safety-Gymnasium Benchmark for on-policy algorithms evaluates the effectiveness of OmniSafe's on-policy algorithms across multiple environments from the Safety-Gymnasium task suite. For each supported algorithm and environment, we offer the following:

Default hyperparameters used for the benchmark and scripts that enable result replication.
Performance comparison with other open-source implementations.
Graphs and raw data that can be utilized for research purposes.
Detailed logs obtained during training.
Suggestions and hints on fine-tuning the algorithm for achieving optimal results.

Supported algorithms are listed below:

First-Order

[NIPS 1999] Policy Gradient (PG)
[Preprint 2017]Proximal Policy Optimization (PPO)
The Lagrange version of PPO (PPOLag)
[IJCAI 2022] Penalized Proximal Policy Optimization for Safe Reinforcement Learning (P3O)
[NeurIPS 2020] First Order Constrained Optimization in Policy Space (FOCOPS)
[NeurIPS 2022] Constrained Update Projection Approach to Safe Policy Optimization (CUP)

Second-Order

[NeurIPS 2001] A Natural Policy Gradient (NaturalPG))
[PMLR 2015] Trust Region Policy Optimization (TRPO)
The Lagrange version of TRPO (TRPOLag)
[ICML 2017] Constrained Policy Optimization (CPO)
[ICML 2017] Proximal Constrained Policy Optimization (PCPO)
[ICLR 2019] Reward Constrained Policy Optimization (RCPO)

Saute RL

[ICML 2022] Sauté RL: Almost Surely Safe Reinforcement Learning Using State Augmentation (PPOSaute, TRPOSaute)

Simmer

[NeurIPS 2022] Effects of Safety State Augmentation on Safe Exploration (PPOSimmerPID, TRPOSimmerPID)

PID-Lagrangian

[ICML 2020] Responsive Safety in Reinforcement Learning by PID Lagrangian Methods (CPPOPID, TRPOPID)

Early Terminated MDP

[Preprint 2021] Safe Exploration by Solving Early Terminated MDP (PPOEarlyTerminated, TRPOEarlyTerminated)

Safety-Gymnasium

We highly recommend using Safety-Gymnasium to run the following experiments. To install, in a linux machine, type:

pip install safety_gymnasium

Run the Benchmark

You can set the main function of examples/benchmarks/experiment_grid.py as:

if __name__ == '__main__':
    eg = ExperimentGrid(exp_name='On-Policy-Benchmarks')

    # set up the algorithms.
    base_policy = ['PolicyGradient', 'NaturalPG', 'TRPO', 'PPO']
    naive_lagrange_policy = ['PPOLag', 'TRPOLag', 'RCPO']
    first_order_policy = ['CUP', 'FOCOPS', 'P3O']
    second_order_policy = ['CPO', 'PCPO']
    saute_policy = ['PPOSaute', 'TRPOSaute']
    simmer_policy = ['PPOSimmerPID', 'TRPOSimmerPID']
    pid_policy = ['CPPOPID', 'TRPOPID']
    early_mdp_policy = ['PPOEarlyTerminated', 'TRPOEarlyTerminated']

    eg.add(
        'algo',
        base_policy +
        naive_lagrange_policy +
        first_order_policy +
        second_order_policy +
        saute_policy +
        simmer_policy +
        pid_policy +
        early_mdp_policy
    )

    # you can use wandb to monitor the experiment.
    eg.add('logger_cfgs:use_wandb', [False])
    # you can use tensorboard to monitor the experiment.
    eg.add('logger_cfgs:use_tensorboard', [True])

    # the default configs here are as follows:
    # eg.add('algo_cfgs:steps_per_epoch', [20000])
    # eg.add('train_cfgs:total_steps', [20000 * 500])
    # which can reproduce results of 1e7 steps.

    # if you want to reproduce results of 1e6 steps, using
    # eg.add('algo_cfgs:steps_per_epoch', [2048])
    # eg.add('train_cfgs:total_steps', [2048 * 500])

    # set the device.
    avaliable_gpus = list(range(torch.cuda.device_count()))
    # if you want to use GPU, please set gpu_id like follows:
    # gpu_id = [0, 1, 2, 3]
    # if you want to use CPU, please set gpu_id = None
    # we recommends using CPU to obtain results as consistent
    # as possible with our publicly available results,
    # since the performance of all on-policy algorithms
    # in OmniSafe is tested on CPU.
    gpu_id = None

    if gpu_id and not set(gpu_id).issubset(avaliable_gpus):
        warnings.warn('The GPU ID is not available, use CPU instead.', stacklevel=1)
        gpu_id = None

    # set up the environment.
    eg.add('env_id', [
        'SafetyHopperVelocity-v1',
        'SafetyWalker2dVelocity-v1',
        'SafetySwimmerVelocity-v1',
        'SafetyAntVelocity-v1',
        'SafetyHalfCheetahVelocity-v1',
        'SafetyHumanoidVelocity-v1'
        ])
    eg.add('seed', [0, 5, 10, 15, 20])

    # total experiment num must can be divided by num_pool.
    # meanwhile, users should decide this value according to their machine.
    eg.run(train, num_pool=5, gpu_id=gpu_id)

After that, you can run the following command to run the benchmark:

cd examples/benchmarks
python run_experiment_grid.py

You can also plot the results by running the following command:

cd examples
python analyze_experiment_results.py

For a detailed usage of OmniSafe statistics tool, please refer to this tutorial.

Logs is saved in examples/benchmarks/exp-x and can be monitored with tensorboard or wandb.

tensorboard --logdir examples/benchmarks/exp-x

After the experiment is finished, you can use the following command to generate the video of the trained agent:

cd examples
python evaluate_saved_policy.py

Please note that before you evaluate, set the LOG_DIR in evaluate_saved_policy.py.

For example, if I train PPOLag in SafetyHumanoidVelocity-v1

LOG_DIR = '~/omnisafe/examples/runs/PPOLag-<SafetyHumanoidVelocity-v1>/seed-000'
play = True
save_replay = True
if __name__ == '__main__':
    evaluator = omnisafe.Evaluator(play=play, save_replay=save_replay)
    for item in os.scandir(os.path.join(LOG_DIR, 'torch_save')):
        if item.is_file() and item.name.split('.')[-1] == 'pt':
            evaluator.load_saved(
                save_dir=LOG_DIR, model_name=item.name, camera_name='track', width=256, height=256
            )
            evaluator.render(num_episodes=1)
            evaluator.evaluate(num_episodes=1)

OmniSafe Benchmark

Classic Reinforcement Learning Algorithms

To ascertain the credibility of OmniSafe ’s algorithmic implementation, a comparative assessment was conducted, juxtaposing the performance of classical reinforcement learning algorithms. Such as Policy Gradient, Natural Policy Gradient, TRPO and PPO. The performance table is provided in Table 1. with well-established open-source implementations, specifically Tianshou and Stable-Baselines3.

Table 1:The performance of OmniSafe, which was evaluated in relation to published baselines within the Safety-Gymnasium MuJoCo Velocity environments. Experimental outcomes, comprising mean and standard deviation, were derived from 10 assessment iterations encompassing multiple random seeds. A noteworthy distinction lies in the fact that Stable-Baselines3 employs distinct parameters tailored to each environment, while OmniSafe maintains a consistent parameter set across all environments.

	Policy Gradient			PPO
Environment	OmniSafe (Ours)	Tianshou	Stable-Baselines3	OmniSafe (Ours)	Tianshou	Stable-Baselines3
SafetyAntVelocity-v1	2769.45 ± 550.71	145.33 ± 127.55	- ± -	4295.96 ± 658.2	2607.48 ± 1415.78	1780.61 ± 780.65
SafetyHalfCheetahVelocity-v1	2625.44 ± 1079.04	707.56 ± 158.59	- ± -	3507.47 ± 1563.69	6299.27 ± 1692.38	5074.85 ± 2225.47
SafetyHopperVelocity-v1	1884.38 ± 825.13	343.88 ± 51.85	- ± -	2679.98 ± 921.96	1834.7 ± 862.06	838.96 ± 351.10
SafetyHumanoidVelocity-v1	647.52 ± 154.82	438.97 ± 123.68	- ± -	1106.09 ± 607.6	677.43 ± 189.96	762.73 ± 170.22
SafetySwimmerVelocity-v1	47.31 ± 16.19	27.12 ± 7.47	- ± -	113.28 ± 20.22	37.93 ± 8.68	273.86 ± 87.76
SafetyWalker2dVelocity-v1	1665 .00 ± 930.18	373.63 ± 129.2	- ± -	3806.39 ± 1547.48	3748.26 ± 1832.83	3304.35 ± 706.13
	NaturalPG			TRPO
Environment	OmniSafe (Ours)	Tianshou	Stable-Baselines3	OmniSafe (Ours)	Tianshou	Stable-Baselines3
SafetyAntVelocity-v1	3793.70 ± 583.66	2062.45 ± 876.43	- ± -	4362.43 ± 640.54	2521.36 ± 1442.10	3233.58 ± 1437.16
SafetyHalfCheetahVelocity-v1	4096.77 ± 1223.70	3430.9 ± 239.38	- ± -	3313.31 ± 1048.78	4255.73 ± 1053.82	7185.06 ± 3650.82
SafetyHopperVelocity-v1	2590.54 ± 631.05	993.63 ± 489.42	- ± -	2698.19 ± 568.80	1346.94 ± 984.09	2467.10 ± 1160.25
SafetyHumanoidVelocity-v1	3838.67 ± 1654.79	810.76 ± 270.69	- ± -	1461.51 ± 602.23	749.42 ± 149.81	2828.18 ± 2256.38
SafetySwimmerVelocity-v1	116.33 ± 5.97	29.75 ± 12.00	- ± -	105.08 ± 31.00	37.21 ± 4.04	258.62 ± 124.91
SafetyWalker2dVelocity-v1	4054.62 ± 1266.76	3372.59 ± 1049.14	- ± -	4099.97 ± 409.05	3372.59 ± 961.74	4227.91 ± 760.93

Safe Reinforcement Learning Algorithms

To demonstrate the high reliability of the algorithms implemented, OmniSafe offers performance insights within the Safety-Gymnasium environment. It should be noted that all data is procured under the constraint of cost_limit=25.00. The results are presented in Table 2 and the training curves are in the following sections (Please click the triangle button to see the training curves).

Performance Table

Table 2: The performance of OmniSafe on-policy algorithms, encompassing both reward and cost, was assessed within the Safety-Gymnasium environments. It is crucial to highlight that all on-policy algorithms underwent evaluation following 1e7 training steps.

	Policy Gradient		Natural PG		TRPO		PPO
Environment	Reward	Cost	Reward	Cost	Reward	Cost	Reward	Cost
SafetyAntVelocity-v1	5292.29 ± 913.44	919.42 ± 158.61	5547.20 ± 807.89	895.56 ± 77.13	6026.79 ± 314.98	933.46 ± 41.28	5977.73 ± 885.65	958.13 ± 134.5
SafetyHalfCheetahVelocity-v1	5188.46 ± 1202.76	896.55 ± 184.7	5878.28 ± 2012.24	847.74 ± 249.02	6490.76 ± 2507.18	734.26 ± 321.88	6921.83 ± 1721.79	919.2 ± 173.08
SafetyHopperVelocity-v1	3218.17 ± 672.88	881.76 ± 198.46	2613.95 ± 866.13	587.78 ± 220.97	2047.35 ± 447.33	448.12 ± 103.87	2337.11 ± 942.06	550.02 ± 237.70
SafetyHumanoidVelocity-v1	7001.78 ± 419.67	834.11 ± 212.43	8055.20 ± 641.67	946.40 ± 9.11	8681.24 ± 3934.08	718.42 ± 323.30	9115.93 ± 596.88	960.44 ± 7.06
SafetySwimmerVelocity-v1	77.05 ± 33.44	107.1 ± 60.58	120.19 ± 7.74	161.78 ± 17.51	124.91 ± 6.13	176.56 ± 15.95	119.77 ± 13.8	165.27 ± 20.15
SafetyWalker2dVelocity-v1	4832.34 ± 685.76	866.59 ± 93.47	5347.35 ± 436.86	914.74 ± 32.61	6096.67 ± 723.06	914.46 ± 27.85	6239.52 ± 879.99	902.68 ± 100.93
SafetyCarGoal1-v0	35.86 ± 1.97	57.46 ± 48.34	36.07 ± 1.25	58.06 ± 10.03	36.60 ± 0.22	55.58 ± 12.68	33.41 ± 2.89	58.06 ± 42.06
SafetyCarButton1-v0	19.76 ± 10.15	353.26 ± 177.08	22.16 ± 4.48	333.98 ± 67.49	21.98 ± 2.06	343.22 ± 24.60	17.51 ± 9.46	373.98 ± 156.64
SafetyCarGoal2-v0	29.43 ± 4.62	179.2 ± 84.86	30.26 ± 0.38	209.62 ± 29.97	32.17 ± 1.24	190.74 ± 21.05	29.88 ± 4.55	194.16 ± 106.2
SafetyCarButton2-v0	18.06 ± 10.53	349.82 ± 187.07	20.85 ± 3.14	313.88 ± 58.20	20.51 ± 3.34	316.42 ± 35.28	21.35 ± 8.22	312.64 ± 138.4
SafetyPointGoal1-v0	26.19 ± 3.44	201.22 ± 80.4	26.92 ± 0.58	57.92 ± 9.97	27.20 ± 0.44	45.88 ± 11.27	25.44 ± 5.43	55.72 ± 35.55
SafetyPointButton1-v0	29.98 ± 5.24	141.74 ± 75.13	31.95 ± 1.53	123.98 ± 32.05	30.61 ± 0.40	134.38 ± 22.06	27.03 ± 6.14	152.48 ± 80.39
SafetyPointGoal2-v0	25.18 ± 3.62	204.96 ± 104.97	26.19 ± 0.84	193.60 ± 18.54	25.61 ± 0.89	202.26 ± 15.15	25.49 ± 2.46	159.28 ± 87.13
SafetyPointButton2-v0	26.88 ± 4.38	153.88 ± 65.54	28.45 ± 1.49	160.40 ± 20.08	28.78 ± 2.05	170.30 ± 30.59	25.91 ± 6.15	166.6 ± 111.21
	RCPO		TRPOLag		PPOLag		P3O
Environment	Reward	Cost	Reward	Cost	Reward	Cost	Reward	Cost
SafetyAntVelocity-v1	3139.52 ± 110.34	12.34 ± 3.11	3041.89 ± 180.77	19.52 ± 20.21	3261.87 ± 80.00	12.05 ± 6.57	2636.62 ± 181.09	20.69 ± 10.23
SafetyHalfCheetahVelocity-v1	2440.97 ± 451.88	9.02 ± 9.34	2884.68 ± 77.47	9.04 ± 11.83	2946.15 ± 306.35	3.44 ± 4.77	2117.84 ± 313.55	27.6 ± 8.36
SafetyHopperVelocity-v1	1428.58 ± 199.87	11.12 ± 12.66	1391.79 ± 269.07	11.22 ± 9.97	961.92 ± 752.87	13.96 ± 19.33	1231.52 ± 465.35	16.33 ± 11.38
SafetyHumanoidVelocity-v1	6286.51 ± 151.03	19.47 ± 7.74	6551.30 ± 58.42	59.56 ± 117.37	6624.46 ± 25.9	5.87 ± 9.46	6342.47 ± 82.45	126.4 ± 193.76
SafetySwimmerVelocity-v1	61.29 ± 18.12	22.60 ± 1.16	81.18 ± 16.33	22.24 ± 3.91	64.74 ± 17.67	28.02 ± 4.09	38.02 ± 34.18	18.4 ± 12.13
SafetyWalker2dVelocity-v1	3064.43 ± 218.83	3.02 ± 1.48	3207.10 ± 7.88	14.98 ± 9.27	2982.27 ± 681.55	13.49 ± 14.55	2713.57 ± 313.2	20.51 ± 14.09
SafetyCarGoal1-v0	18.71 ± 2.72	23.10 ± 12.57	27.04 ± 1.82	26.80 ± 5.64	13.27 ± 9.26	21.72 ± 32.06	-1.10 ± 6.851	50.58 ± 99.24
SafetyCarButton1-v0	-2.04 ± 2.98	43.48 ± 31.52	-0.38 ± 0.85	37.54 ± 31.72	0.33 ± 1.96	55.5 ± 89.64	-2.06 ± 7.2	43.78 ± 98.01
SafetyCarGoal2-v0	2.30 ± 1.76	22.90 ± 16.22	3.65 ± 1.09	39.98 ± 20.29	1.58 ± 2.49	13.82 ± 24.62	-0.07 ± 1.62	43.86 ± 99.58
SafetyCarButton2-v0	-1.35 ± 2.41	42.02 ± 31.77	-1.68 ± 2.55	20.36 ± 13.67	0.76 ± 2.52	47.86 ± 103.27	0.11 ± 0.72	85.94 ± 122.01
SafetyPointGoal1-v0	15.27 ± 4.05	30.56 ± 19.15	18.51 ± 3.83	22.98 ± 8.45	12.96 ± 6.95	25.80 ± 34.99	1.6 ± 3.01	31.1 ± 80.03
SafetyPointButton1-v0	3.65 ± 4.47	26.30 ± 9.22	6.93 ± 1.84	31.16 ± 20.58	4.60 ± 4.73	20.8 ± 35.78	-0.34 ± 1.53	52.86 ± 85.62
SafetyPointGoal2-v0	2.17 ± 1.46	33.82 ± 21.93	4.64 ± 1.43	26.00 ± 4.70	1.98 ± 3.86	41.20 ± 61.03	0.34 ± 2.2	65.84 ± 195.76
SafetyPointButton2-v0	7.18 ± 1.93	45.02 ± 25.28	5.43 ± 3.44	25.10 ± 8.98	0.93 ± 3.69	33.72 ± 58.75	0.33 ± 2.44	28.5 ± 49.79
	CUP		PCPO		FOCOPS		CPO
Environment	Reward	Cost	Reward	Cost	Reward	Cost	Reward	Cost
SafetyAntVelocity-v1	3215.79 ± 346.68	18.25 ± 17.12	2257.07 ± 47.97	10.44 ± 5.22	3184.48 ± 305.59	14.75 ± 6.36	3098.54 ± 78.90	14.12 ± 3.41
SafetyHalfCheetahVelocity-v1	2850.6 ± 244.65	4.27 ± 4.46	1677.93 ± 217.31	19.06 ± 15.26	2965.2 ± 290.43	2.37 ± 3.5	2786.48 ± 173.45	4.70 ± 6.72
SafetyHopperVelocity-v1	1716.08 ± 5.93	7.48 ± 5.535	1551.22 ± 85.16	15.46 ± 9.83	1437.75 ± 446.87	10.13 ± 8.87	1713.71 ± 18.26	13.40 ± 5.82
SafetyHumanoidVelocity-v1	6109.94 ± 497.56	24.69 ± 20.54	5852.25 ± 78.01	0.24 ± 0.48	6489.39 ± 35.1	13.86 ± 39.33	6465.34 ± 79.87	0.18 ± 0.36
SafetySwimmerVelocity-v1	63.83 ± 46.45	21.95 ± 11.04	54.42 ± 38.65	17.34 ± 1.57	53.87 ± 17.9	29.75 ± 7.33	65.30 ± 43.25	18.22 ± 8.01
SafetyWalker2dVelocity-v1	2466.95 ± 1114.13	6.63 ± 8.25	1802.86 ± 714.04	18.82 ± 5.57	3117.05 ± 53.60	8.78 ± 12.38	2074.76 ± 962.45	21.90 ± 9.41
SafetyCarGoal1-v0	6.14 ± 6.97	36.12 ± 89.56	21.56 ± 2.87	38.42 ± 8.36	15.23 ± 10.76	31.66 ± 93.51	25.52 ± 2.65	43.32 ± 14.35
SafetyCarButton1-v0	1.49 ± 2.84	103.24 ± 123.12	0.36 ± 0.85	40.52 ± 21.25	0.21 ± 2.27	31.78 ± 47.03	0.82 ± 1.60	37.86 ± 27.41
SafetyCarGoal2-v0	1.78 ± 4.03	95.4 ± 129.64	1.62 ± 0.56	48.12 ± 31.19	2.09 ± 4.33	31.56 ± 58.93	3.56 ± 0.92	32.66 ± 3.31
SafetyCarButton2-v0	1.49 ± 2.64	173.68 ± 163.77	0.66 ± 0.42	49.72 ± 36.50	1.14 ± 3.18	46.78 ± 57.47	0.17 ± 1.19	48.56 ± 29.34
SafetyPointGoal1-v0	14.42 ± 6.74	19.02 ± 20.08	18.57 ± 1.71	22.98 ± 6.56	14.97 ± 9.01	33.72 ± 42.24	20.46 ± 1.38	28.84 ± 7.76
SafetyPointButton1-v0	3.5 ± 7.07	39.56 ± 54.26	2.66 ± 1.83	49.40 ± 36.76	5.89 ± 7.66	38.24 ± 42.96	4.04 ± 4.54	40.00 ± 4.52
SafetyPointGoal2-v0	1.06 ± 2.67	107.3 ± 204.26	1.06 ± 0.69	51.92 ± 47.40	2.21 ± 4.15	37.92 ± 111.81	2.50 ± 1.25	40.84 ± 23.31
SafetyPointButton2-v0	2.88 ± 3.65	54.24 ± 71.07	1.05 ± 1.27	41.14 ± 12.35	2.43 ± 3.33	17.92 ± 26.1	5.09 ± 1.83	48.92 ± 17.79
	PPOSaute		TRPOSaute		PPOSimmerPID		TRPOSimmerPID
Environment	Reward	Cost	Reward	Cost	Reward	Cost	Reward	Cost
SafetyAntVelocity-v1	2978.74 ± 93.65	16.77 ± 0.92	2507.65 ± 63.97	8.036 ± 0.39	2944.84 ± 60.53	16.20 ± 0.66	3018.95 ± 66.44	16.52 ± 0.23
SafetyHalfCheetahVelocity-v1	2901.40 ± 25.49	16.20 ± 0.60	2521.80 ± 477.29	7.61 ± 0.39	2922.17 ± 24.84	16.14 ± 0.14	2737.79 ± 37.53	16.44 ± 0.21
SafetyHopperVelocity-v1	1650.91 ± 152.65	17.87 ± 1.33	1368.28 ± 576.08	10.38 ± 4.38	1699.94 ± 24.25	17.04 ± 0.41	1608.41 ± 88.23	16.30 ± 0.30
SafetyHumanoidVelocity-v1	6401.00 ± 32.23	17.10 ± 2.41	5759.44 ± 75.73	15.84 ± 1.42	6401.85 ± 57.62	11.06 ± 5.35	6411.32 ± 44.26	13.04 ± 2.68
SafetySwimmerVelocity-v1	35.61 ± 4.37	3.44 ± 1.35	34.72 ± 1.37	10.19 ± 2.32	77.52 ± 40.20	0.98 ± 1.91	51.39 ± 40.09	0.00 ± 0.00
SafetyWalker2dVelocity-v1	2410.89 ± 241.22	18.88 ± 2.38	2548.82 ± 891.65	13.21 ± 6.09	3187.56 ± 32.66	17.10 ± 0.49	3156.99 ± 30.93	17.14 ± 0.54
SafetyCarGoal1-v0	7.12 ± 5.41	21.68 ± 29.11	16.67 ± 10.57	23.58 ± 26.39	8.45 ± 7.16	18.98 ± 25.63	15.08 ± 13.41	23.22 ± 19.80
SafetyCarButton1-v0	-1.72 ± 0.89	51.88 ± 28.18	-2.03 ± 0.40	6.24 ± 6.14	-0.57 ± 0.63	49.14 ± 37.77	-1.24 ± 0.47	17.26 ± 16.13
SafetyCarGoal2-v0	0.90 ± 1.20	19.98 ± 10.12	1.76 ± 5.20	31.50 ± 45.50	1.02 ± 1.41	27.32 ± 60.12	0.93 ± 2.21	26.66 ± 60.07
SafetyCarButton2-v0	-1.89 ± 1.86	47.33 ± 28.90	-2.60 ± 0.40	74.57 ± 84.95	-1.31 ± 0.93	52.33 ± 19.96	-0.99 ± 0.63	20.40 ± 12.77
SafetyPointGoal1-v0	7.06 ± 5.85	20.04 ± 21.91	16.18 ± 9.55	29.94 ± 26.68	8.30 ± 6.03	25.32 ± 31.91	11.64 ± 8.46	30.00 ± 27.67
SafetyPointButton1-v0	-1.47 ± 0.98	22.60 ± 13.91	-3.13 ± 3.51	9.04 ± 3.94	-1.97 ± 1.41	12.80 ± 7.84	-1.36 ± 0.37	2.14 ± 1.73
SafetyPointGoal2-v0	0.84 ± 2.93	14.06 ± 30.21	1.64 ± 4.02	19.00 ± 34.69	0.56 ± 2.52	12.36 ± 43.39	1.55 ± 4.68	14.90 ± 27.82
SafetyPointButton2-v0	-1.38 ± 0.11	12.00 ± 8.60	-2.56 ± 0.67	17.27 ± 10.01	-1.70 ± 0.29	7.90 ± 3.30	-1.66 ± 0.99	6.70 ± 4.74
	CPPOPID		TRPOPID		PPOEarlyTerminated		TRPOEarlyTerminated
Environment	Reward	Cost	Reward	Cost	Reward	Cost	Reward	Cost
SafetyAntVelocity-v1	3213.36 ± 146.78	14.30 ± 7.39	3052.94 ± 139.67	15.22 ± 3.68	2801.53 ± 19.66	0.23 ± 0.09	3052.63 ± 58.41	0.40 ± 0.23
SafetyHalfCheetahVelocity-v1	2837.89 ± 398.52	8.06 ± 9.62	2796.75 ± 190.84	11.16 ± 9.80	2447.25 ± 346.84	3.47 ± 4.90	2555.70 ± 368.17	0.06 ± 0.08
SafetyHopperVelocity-v1	1713.29 ± 10.21	8.96 ± 4.28	1178.59 ± 646.71	18.76 ± 8.93	1643.39 ± 2.58	0.77 ± 0.26	1646.47 ± 49.95	0.42 ± 0.84
SafetyHumanoidVelocity-v1	6579.26 ± 55.70	3.76 ± 3.61	6407.95 ± 254.06	7.38 ± 11.34	6321.45 ± 35.73	0.00 ± 0.00	6332.14 ± 89.86	0.00 ± 0.00
SafetySwimmerVelocity-v1	91.05 ± 62.68	19.12 ± 8.33	69.75 ± 46.52	20.48 ± 9.13	33.02 ± 7.26	24.23 ± 0.54	39.24 ± 5.01	23.20 ± 0.48
SafetyWalker2dVelocity-v1	2183.43 ± 1300.69	14.12 ± 10.28	2707.75 ± 980.56	9.60 ± 8.94	2195.57 ± 1046.29	7.63 ± 10.44	2079.64 ± 1028.73	13.74 ± 15.94
SafetyCarGoal1-v0	10.60 ± 2.51	30.66 ± 7.53	25.49 ± 1.31	28.92 ± 7.66	17.92 ± 1.54	21.60 ± 0.83	22.09 ± 3.07	17.97 ± 1.35
SafetyCarButton1-v0	-1.36 ± 0.68	14.62 ± 9.40	-0.31 ± 0.49	15.24 ± 17.01	4.47 ± 1.12	25.00 ± 0.00	4.34 ± 0.72	25.00 ± 0.00
SafetyCarGoal2-v0	0.13 ± 1.11	23.50 ± 1.22	1.77 ± 1.20	17.43 ± 12.13	6.59 ± 0.58	25.00 ± 0.00	7.12 ± 4.06	23.37 ± 1.35
SafetyCarButton2-v0	-1.59 ± 0.70	39.97 ± 26.91	-2.95 ± 4.03	27.90 ± 6.37	4.86 ± 1.57	25.00 ± 0.00	5.07 ± 1.24	25.00 ± 0.00
SafetyPointGoal1-v0	8.43 ± 3.43	25.74 ± 7.83	19.24 ± 3.94	21.38 ± 6.96	16.03 ± 8.60	19.17 ± 9.42	16.31 ± 6.99	22.10 ± 6.13
SafetyPointButton1-v0	1.18 ± 1.02	29.42 ± 12.10	6.40 ± 1.43	27.90 ± 13.27	7.48 ± 8.47	24.27 ± 3.95	9.52 ± 7.86	25.00 ± 0.00
SafetyPointGoal2-v0	-0.56 ± 0.06	48.43 ± 40.55	1.67 ± 1.43	23.50 ± 11.17	6.09 ± 5.03	25.00 ± 0.00	8.62 ± 7.13	25.00 ± 0.00
SafetyPointButton2-v0	0.42 ± 0.63	28.87 ± 11.27	1.00 ± 1.00	30.00 ± 9.50	6.94 ± 4.47	25.00 ± 0.00	8.35 ± 10.44	25.00 ± 0.00

First Order Algorithms

1e6 Steps Velocity Results

SafetyAntVelocity-v1

SafetyHalfCheetahVelocity-v1

SafetyHopperVelocity-v1

SafetyHumanoidVelocity-v1

SafetyWalker2dVelocity-v1

SafetySwimmerVelocity-v1

Figure 1.1: Training curves in Safety-Gymnasium MuJoCo Velocity environments within 1e6 steps

1e7 Steps Velocity Results

SafetyAntVelocity-v1

SafetyHalfCheetahVelocity-v1

SafetyHopperVelocity-v1

SafetyHumanoidVelocity-v1

SafetyWalker2dVelocity-v1

SafetySwimmerVelocity-v1

Figure 1.2: Training curves in Safety-Gymnasium MuJoCo Velocity environments within 1e7 steps

1e7 Steps Navigation Results

SafetyCarButton1-v0

SafetyCarButton2-v0

SafetyCarGoal1-v0

SafetyCarGoal2-v0

SafetyPointButton1-v0

SafetyPointButton2-v0

SafetyPointGoal1-v0

SafetyPointGoal2-v0

Figure 1.3: Training curves in Safety-Gymnasium MuJoCo Navigation environments within 1e7 steps

Second Order Algorithms

1e6 Steps Velocity Results

SafetyAntVelocity-v1

SafetyHalfCheetahVelocity-v1

SafetyHopperVelocity-v1

SafetyHumanoidVelocity-v1

SafetyWalker2dVelocity-v1

SafetySwimmerVelocity-v1

Figure 2.1: Training curves of second order algorithms in Safety-Gymnasium MuJoCo Velocity environments within 1e6 steps

1e7 Steps Velocity Results

SafetyAntVelocity-v1

SafetyHalfCheetahVelocity-v1

SafetyHopperVelocity-v1

SafetyHumanoidVelocity-v1

SafetyWalker2dVelocity-v1

SafetySwimmerVelocity-v1

Figure 2.2: Training curves of second order algorithms in Safety-Gymnasium MuJoCo Velocity environments within 1e7 steps

1e7 Steps Navigation Results

SafetyCarButton1-v0

SafetyCarButton2-v0

SafetyCarGoal1-v0

SafetyCarGoal2-v0

SafetyPointButton1-v0

SafetyPointButton2-v0

SafetyPointGoal1-v0

SafetyPointGoal2-v0

Figure 2.3: Training curves of second order algorithms in Safety-Gymnasium MuJoCo Navigation environments within 1e7 steps

Saute Algorithms

1e6 Steps Velocity Results

SafetyAntVelocity-v1

SafetyHalfCheetahVelocity-v1

SafetyHopperVelocity-v1

SafetyHumanoidVelocity-v1

SafetyWalker2dVelocity-v1

SafetySwimmerVelocity-v1

Figure 3.1: Training curves of Saute MDP algorithms in Safety-Gymnasium MuJoCo Velocity environments within 1e6 steps

1e7 Steps Velocity Results

SafetyAntVelocity-v1

SafetyHalfCheetahVelocity-v1

SafetyHopperVelocity-v1

SafetyHumanoidVelocity-v1

SafetyWalker2dVelocity-v1

SafetySwimmerVelocity-v1

Figure 3.2: Training curves of Saute MDP algorithms in Safety-Gymnasium MuJoCo Velocity environments within 1e7 steps

1e7 Steps Navigation Results

SafetyCarButton1-v0

SafetyCarButton2-v0

SafetyCarCircle1-v0

SafetyCarCircle2-v0

SafetyCarGoal1-v0

SafetyCarGoal2-v0

SafetyPointButton1-v0

SafetyPointButton2-v0

SafetyPointCircle1-v0

SafetyPointCircle2-v0

SafetyPointGoal1-v0

SafetyPointGoal2-v0

Figure 3.3: Training curves of Saute MDP algorithms in Safety-Gymnasium MuJoCo Navigation environments within 1e7 steps

Simmer Algorithms

1e6 Steps Velocity Results

SafetyAntVelocity-v1

SafetyHalfCheetahVelocity-v1

SafetyHopperVelocity-v1

SafetyHumanoidVelocity-v1

SafetyWalker2dVelocity-v1

SafetySwimmerVelocity-v1

Figure 4.1: Training curves of Simmer MDP algorithms in Safety-Gymnasium MuJoCo Velocity environments within 1e6 steps

1e7 Steps Velocity Results

SafetyAntVelocity-v1

SafetyHalfCheetahVelocity-v1

SafetyHopperVelocity-v1

SafetyHumanoidVelocity-v1

SafetyWalker2dVelocity-v1

SafetySwimmerVelocity-v1

Figure 4.2: Training curves of Simmer MDP algorithms in Safety-Gymnasium MuJoCo Velocity environments within 1e7 steps

1e7 Steps Navigation Results

SafetyCarButton1-v0

SafetyCarButton2-v0

SafetyCarGoal1-v0

SafetyCarGoal2-v0

SafetyPointButton1-v0

SafetyPointButton2-v0

SafetyPointGoal1-v0

SafetyPointGoal2-v0

Figure 4.3: Training curves of Simmer MDP algorithms in Safety-Gymnasium MuJoCo Navigation environments within 1e7 steps

PID-Lagrangian Algorithms

1e6 Steps Velocity Results

SafetyAntVelocity-v1

SafetyHalfCheetahVelocity-v1

SafetyHopperVelocity-v1

SafetyHumanoidVelocity-v1

SafetyWalker2dVelocity-v1

SafetySwimmerVelocity-v1

Figure 5.1: Training curves of PID-Lagrangian algorithms in Safety-Gymnasium MuJoCo Velocity environments within 1e6 steps

1e7 Steps Velocity Results

SafetyAntVelocity-v1

SafetyHalfCheetahVelocity-v1

SafetyHopperVelocity-v1

SafetyHumanoidVelocity-v1

SafetyWalker2dVelocity-v1

SafetySwimmerVelocity-v1

Figure 5.2: Training curves of PID-Lagrangian algorithms in Safety-Gymnasium MuJoCo Velocity environments within 1e7 steps

1e7 Steps Navigation Results

SafetyCarButton1-v0

SafetyCarButton2-v0

SafetyCarGoal1-v0

SafetyCarGoal2-v0

SafetyPointButton1-v0

SafetyPointButton2-v0

SafetyPointGoal1-v0

SafetyPointGoal2-v0

Figure 5.3: Training curves of PID-Lagrangian algorithms in Safety-Gymnasium MuJoCo Navigation environments within 1e7 steps.

Early Terminated MDP Algorithms

1e6 Steps Velocity Results

SafetyAntVelocity-v1

SafetyHalfCheetahVelocity-v1

SafetyHopperVelocity-v1

SafetyHumanoidVelocity-v1

SafetyWalker2dVelocity-v1

SafetySwimmerVelocity-v1

Figure 6.1: Training curves of early terminated MDP algorithms in Safety-Gymnasium MuJoCo Velocity environments within 1e6 steps.

1e7 Steps Velocity Results

SafetyAntVelocity-v1

SafetyHalfCheetahVelocity-v1

SafetyHopperVelocity-v1

SafetyHumanoidVelocity-v1

SafetyWalker2dVelocity-v1

SafetySwimmerVelocity-v1

Figure 6.2: Training curves of early terminated MDP algorithms in Safety-Gymnasium MuJoCo Velocity environments within 1e7 steps.

1e7 Steps Navigation Results

SafetyCarButton1-v0

SafetyCarButton2-v0

SafetyCarGoal1-v0

SafetyCarGoal2-v0

SafetyPointButton1-v0

SafetyPointButton2-v0

SafetyPointGoal1-v0

SafetyPointGoal2-v0

Figure 6.3: Training curves of early terminated MDP algorithms in Safety-Gymnasium MuJoCo Navigation environments within 1e7 steps.

Experiment Analysis

Hyperparameters

We are continuously improving performance for on-policy algorithms and finding better hyperparameters. So we are happy to receive any advice from users, feel free for opening an issue or pull request.

First-Order Methods Specific Hyperparameters

algo_cfgs:kl_early_stop: Whether to use the early stop trick for KL divergence. In first-order methods, this parameter is set to True. If the KL divergence is too large, we will stop the updating iteration.

Second-Order Methods Specific Hyperparameters

algo_cfgs:kl_early_stop: Whether to use early stop for KL divergence. In second-order methods, we use line search to find the proper step size. If the KL divergence is too large, we will stop the line search and use the previous step size. So it is not necessary to use the early stop trick for KL divergence in second-order methods. We set kl_early_stop=False in second-order methods.
model_cfgs:actor:lr: The learning rate of the policy network. Second-order methods update the policy by directly setting the parameters. So we just set the learning rate of the policy network to None.

You may find that in some environments, Natural PG performs nearly the same as TRPO. This is because, in the Mujoco Velocity environment series, the TRPO search update step size is always 1. Additionally, since all algorithms were tested under the same series of random seeds, there is an occurrence of TRPO and Natural PG training curves overlapping.

Saute RL Methods Specific Hyperparameters

saute_gamma: In the experiment we found that saute_gamma impacts the performance of Saute RL methods a lot. We found that 0.999 is a good value for this hyperparameter.

Simmer RL Methods Specific Hyperparameters

saute_gamma: Since the Simmer RL methods are based on Saute RL methods, we also set saute_gamma to 0.999.
control_cfgs: The controller parameters of the Simmer RL methods. While Simmer uses a PID controller to control the safety budget, and PID is known as a parameter-sensitive controller. So we need to tune the control parameters (Kp, Ki and Kd) for different environments. We have done some experiments to find control parameters generally suitable for all environments, that is:

Parameters	Descriptions	Values
`kp`	The proportional gain of the PID controller	1.0
`ki`	The derivative gain of the PID controller	0.001
`kd`	The integral gain of the PID controller	0.01
`polyak`	The learning rate for soft update	0.995

PID-Lagrangian Methods Specific Hyperparameters

PID-Lagrangian methods use a PID controller to control the lagrangian multiplier, The pid_kp, pid_kd and pid_ki count for the proportional gain, derivative gain and integral gain of the PID controller respectively. As PID-Lagrangian methods use a PID controller to control the lagrangian multiplier, the hyperparameters of the PID controller are important for the performance of the algorithm.

pid_kp: The proportional gain of the PID controller, determines how much the output responds to changes in the ep_costs signal. If the pid_kp is too large, the lagrangian multiplier will oscillate and the performance will be bad. If the pid_kp is too small, the lagrangian multiplier will update slowly and the performance will also be bad.
pid_kd: The derivative gain of the PID controller, determines how much the output responds to changes in the ep_costs signal. If the pid_kd is too large, the lagrangian multiplier may be too sensitive to noise or changes in the ep_costs signal, leading to instability or oscillations. If the pid_kd is too small, the lagrangian multiplier may not respond quickly or accurately enough to changes in the ep_costs.
pid_ki: The integral gain of the PID controller, determines the controller's ability to eliminate the steady-state error, by integrating the ep_costs signal over time. If the pid_ki is too large, the lagrangian multiplier may become too responsive to errors before.

We have done some experiments to find relatively good pid_kp, pid_ki, and pid_kd for all environments, and we found that the following value is a good value for this hyperparameter.

Parameters	Descriptions	Values
`pid_kp`	The proportional gain of the PID controller	0.1
`pid_ki`	The derivative gain of the PID controller	0.01
`pid_kd`	The integral gain of the PID controller	0.01

Early Terminated MDP Methods Specific Hyperparameters

vector_num_envs: Though vectorized environments can speed up the training process, we found that the early terminated MDP will reset all the environments when one of the agents violates the safety constraint. So we set vector_num_envs to 1 in the early terminated MDP methods.

Lagragian

The lagrangian versions of on-policy algorithms share the same set of lagrangian hyperparameters (Except for PID-Lagrangian). The hyperparameters are listed below:

Hyperparameter	Value
`cost_limit`	25.0
`lagrangian_multiplier_init`	0.001
`lambda_lr`	0.035
`lambda_optimizer`	Adam

Some Hints

In our experiments, we found that some hyperparameters are important for the performance of the algorithm:

obs_normalize: Whether to normalize the observation.
reward_normalize: Whether to normalize the reward.
cost_normalize: Whether to normalize the cost.

We have done some experiments to show the effect of these hyperparameters, and we log the best configuration for each algorithm in each environment. You can check it in the omnisafe/configs/on_policy.

In experiments, we found that the obs_normalize=True always performs better than obs_normalize=False in on-policy algorithms. That means the reward would increase quicker while the safety constraint also maintained if we normalize the observation. So we set obs_normalize=True in almost all on-policy algorithms.

Importantly, we found that the reward_normalize=True does not always perform better than reward_normalize=False, especially in the SafetyHopperVelocity-v1 and SafetyWalker2dVelocity environments.

To improve the overall performance stability, we use the following unified setting in all of OmniSafe on-policy algorithms

Hyperparameter	Value
`obs_normalize`	`True`
`reward_normalize`	`False`
`cost_normalize`	`False`

Besides, the hyperparameter torch_num_threads in train_cfgs is also important. In a single training session, a larger value for torch_num_threads often means faster training speed. However, we found in experiments that setting torch_num_threads too high can cause resource contention between parallel training sessions, resulting in slower overall experiment speed. In the configs file, we set the default value for torch_num_threads to 16, which ensures faster training speed for a single session. If you need to launch multiple training sessions in parallel, please consider your device configuration. For example, suppose your CPU has 2 physical threads per core and has a total of 32 cores, if you are launching 16 training scripts in parallel, you had better not set torch_num_threads to a value greater than 4.

If you find that other hyperparameters perform better, please feel free to open an issue or pull request.

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README.md

OmniSafe's Safety-Gymnasium Benchmark for On-Policy Algorithms

Safety-Gymnasium

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OmniSafe Benchmark

Classic Reinforcement Learning Algorithms

Safe Reinforcement Learning Algorithms

Performance Table

First Order Algorithms

Second Order Algorithms

Saute Algorithms

Simmer Algorithms

PID-Lagrangian Algorithms

Early Terminated MDP Algorithms

Experiment Analysis

Hyperparameters

First-Order Methods Specific Hyperparameters

Second-Order Methods Specific Hyperparameters

Saute RL Methods Specific Hyperparameters

Simmer RL Methods Specific Hyperparameters

PID-Lagrangian Methods Specific Hyperparameters

Early Terminated MDP Methods Specific Hyperparameters

Lagragian

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README.md

OmniSafe's Safety-Gymnasium Benchmark for On-Policy Algorithms

Safety-Gymnasium

Run the Benchmark

OmniSafe Benchmark

Classic Reinforcement Learning Algorithms

Safe Reinforcement Learning Algorithms

Performance Table

First Order Algorithms

Second Order Algorithms

Saute Algorithms

Simmer Algorithms

PID-Lagrangian Algorithms

Early Terminated MDP Algorithms

Experiment Analysis

Hyperparameters

First-Order Methods Specific Hyperparameters

Second-Order Methods Specific Hyperparameters

Saute RL Methods Specific Hyperparameters

Simmer RL Methods Specific Hyperparameters

PID-Lagrangian Methods Specific Hyperparameters

Early Terminated MDP Methods Specific Hyperparameters

Lagragian

Some Hints