Document running the entire benchmarking suite #657
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| # Benchmarking imitation | ||
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| This directory contains Sacred configuration files for benchmarking imitation's algorithms. For v0.3.2, these correspond to the hyperparameters used in the paper [imitation: Clean Imitation Learning Implementations](https://www.rocamonde.com/publication/gleave-imitation-2022/). | ||
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| Configuration files can be loaded either from the CLI or from the Python API. The examples below assume that your current working directory is the root of the `imitation` repository. | ||
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| ## Single benchmark | ||
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| To run a single benchmark from the command line: | ||
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| ```bash | ||
| python -m imitation.scripts.<train_script> <algo> \ | ||
| --name=<name> with benchmarking/<config_name>.json | ||
| ``` | ||
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| `train_script` can be either 1) `train_imitation` with `algo` as `bc` or `dagger` or 2) `train_adversarial` with `algo` as `gail` or `airl`. | ||
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| To view the results: | ||
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| ```bash | ||
| python -m imitation.scripts.analyze analyze_imitation with \ | ||
| source_dir_str="output/sacred" table_verbosity=0 \ | ||
| csv_output_path=results.csv \ | ||
| run_name="<name>" | ||
| ``` | ||
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| To run a single benchmark from Python add the config to your Sacred experiment `ex`: | ||
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| ```python | ||
| ... | ||
| ex.add_config('benchmarking/<config_name>.json') | ||
| ``` | ||
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| ## Entire benchmark suite | ||
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| ### Running locally | ||
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| To generate the commands to run the entire benchmarking suite with multiple random seeds: | ||
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| ```bash | ||
| python experiments/commands.py \ | ||
| --name=<name> \ | ||
| --cfg_pattern "benchmarking/example_*.json" \ | ||
| --seeds 0 1 2 \ | ||
| --output_dir=output | ||
| ``` | ||
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| To run those commands in parallel: | ||
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| ```bash | ||
| python experiments/commands.py \ | ||
| --name=<name> \ | ||
| --cfg_pattern "benchmarking/example_*.json" \ | ||
| --seeds 0 1 2 \ | ||
| --output_dir=output | parallel -j 8 | ||
| ``` | ||
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| (You may need to `brew install parallel` to get this to work on Mac.) | ||
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| ### Running on Hofvarpnir | ||
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| To generate the commands for the Hofvarpnir cluster: | ||
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| ```bash | ||
| python experiments/commands.py \ | ||
| --name=<name> \ | ||
| --cfg_pattern "benchmarking/example_*.json" \ | ||
| --seeds 0 1 2 \ | ||
| --output_dir=/data/output \ | ||
| --remote | ||
| ``` | ||
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| To run those commands pipe them into bash: | ||
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| ```bash | ||
| python experiments/commands.py \ | ||
| --name <name> \ | ||
| --cfg_pattern "benchmarking/example_*.json" \ | ||
| --seeds 0 1 2 \ | ||
| --output_dir /data/output \ | ||
| --remote | bash | ||
| ``` | ||
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| ### Results | ||
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| To produce a table with all the results: | ||
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| ```bash | ||
| python -m imitation.scripts.analyze analyze_imitation with \ | ||
| source_dir_str="output/sacred" table_verbosity=0 \ | ||
| csv_output_path=results.csv \ | ||
| run_name="<name>" | ||
| ``` | ||
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| To compute a p-value to test whether the differences from the paper are statistically significant: | ||
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There was a problem hiding this comment. FWIW, in my test run they were statistically significant so something may have changed since the paper There was a problem hiding this comment. Not too surprising, can you share the results and if they moved in a positive or negative direction since the paper? ;) There was a problem hiding this comment. I'll include this once I include the canonical results CSV There was a problem hiding this comment. Actually, I think we should just do a bulk update of all results - I might need help with getting access to more compute for this though There was a problem hiding this comment. Created issue for this #710 There was a problem hiding this comment. Okay for Ant it looks like the original results were mean 1953 std dev 99 and the new results (for me) are mean 1794 std dev 244. The p-value is 0.20 so it's not a statistically significant difference though. This is different from my run yesterday though. This gives more reasons to rerun everything in bulk IMO |
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| ```bash | ||
| python -m imitation.scripts.compare_to_baseline results.csv | ||
| ``` | ||
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| agent_path,checkpoint_interval,seed,show_config,total_timesteps,algorithm_kwargs.demo_batch_size,algorithm_kwargs.gen_replay_buffer_capacity,algorithm_kwargs.n_disc_updates_per_round,common.env_name,common.log_dir,common.log_format_strs,common.log_format_strs_additional.wandb,common.log_level,common.log_root,common.max_episode_steps,common.num_vec,common.parallel,common.wandb.wandb_kwargs.monitor_gym,common.wandb.wandb_kwargs.project,common.wandb.wandb_kwargs.save_code,common.wandb.wandb_name_prefix,common.wandb.wandb_tag,demonstrations.n_expert_demos,demonstrations.rollout_path,expert.policy_type,reward.add_std_alpha,reward.ensemble_size,reward.net_cls.py/type,reward.net_kwargs.normalize_input_layer.py/type,reward.normalize_output_layer.py/type,rl.batch_size,rl.rl_cls.py/type,rl.rl_kwargs.batch_size,rl.rl_kwargs.clip_range,rl.rl_kwargs.ent_coef,rl.rl_kwargs.gae_lambda,rl.rl_kwargs.gamma,rl.rl_kwargs.learning_rate,rl.rl_kwargs.max_grad_norm,rl.rl_kwargs.n_epochs,rl.rl_kwargs.vf_coef,train.n_episodes_eval,train.policy_cls.py/type,train.policy_kwargs.features_extractor_class.py/type,train.policy_kwargs.features_extractor_kwargs.normalize_class.py/type,algo,env_name,expert_return_summary,imit_return_summary | ||
| ,0,101,False,10000000.0,8192,8192,16,seals/Ant-v0,output/airl/seals_Ant-v0/20221024_082122_711915,"['tensorboard', 'stdout', 'wandb']",,20,,,8,True,False,algorithm-benchmark,False,,,,/home/taufeeque/imitation/output/train_experts/2022-09-05T18:27:27-07:00/seals_ant_1/rollouts/final.pkl,ppo-huggingface,,,imitation.rewards.reward_nets.BasicShapedRewardNet,imitation.util.networks.RunningNorm,imitation.util.networks.RunningNorm,8192,stable_baselines3.ppo.ppo.PPO,16,0.3,3.27750078482474e-06,0.8,0.995,3.249429831179079e-05,0.9,10,0.4351450387648799,50,imitation.policies.base.FeedForward32Policy,imitation.policies.base.NormalizeFeaturesExtractor,imitation.util.networks.RunningNorm,AIRL,seals/Ant-v0,2408.22 ± 665.201 (n=104),123.476 ± 2.16606 (n=56) | ||
| ,0,100,False,10000000.0,8192,8192,16,seals/Ant-v0,output/airl/seals_Ant-v0/20221024_082120_c540b2,"['tensorboard', 'stdout', 'wandb']",,20,,,8,True,False,algorithm-benchmark,False,,,,/home/taufeeque/imitation/output/train_experts/2022-09-05T18:27:27-07:00/seals_ant_1/rollouts/final.pkl,ppo-huggingface,,,imitation.rewards.reward_nets.BasicShapedRewardNet,imitation.util.networks.RunningNorm,imitation.util.networks.RunningNorm,8192,stable_baselines3.ppo.ppo.PPO,16,0.3,3.27750078482474e-06,0.8,0.995,3.249429831179079e-05,0.9,10,0.4351450387648799,50,imitation.policies.base.FeedForward32Policy,imitation.policies.base.NormalizeFeaturesExtractor,imitation.util.networks.RunningNorm,AIRL,seals/Ant-v0,2408.22 ± 665.201 (n=104),-378.377 ± 60.6063 (n=56) | ||
| ,0,102,False,10000000.0,8192,8192,16,seals/Ant-v0,output/airl/seals_Ant-v0/20221024_082122_ba94a1,"['tensorboard', 'stdout', 'wandb']",,20,,,8,True,False,algorithm-benchmark,False,,,,/home/taufeeque/imitation/output/train_experts/2022-09-05T18:27:27-07:00/seals_ant_1/rollouts/final.pkl,ppo-huggingface,,,imitation.rewards.reward_nets.BasicShapedRewardNet,imitation.util.networks.RunningNorm,imitation.util.networks.RunningNorm,8192,stable_baselines3.ppo.ppo.PPO,16,0.3,3.27750078482474e-06,0.8,0.995,3.249429831179079e-05,0.9,10,0.4351450387648799,50,imitation.policies.base.FeedForward32Policy,imitation.policies.base.NormalizeFeaturesExtractor,imitation.util.networks.RunningNorm,AIRL,seals/Ant-v0,2408.22 ± 665.201 (n=104),-314.108 ± 19.2371 (n=56) | ||
| ,0,104,False,10000000.0,8192,8192,16,seals/Ant-v0,output/airl/seals_Ant-v0/20221024_082122_8c6aba,"['tensorboard', 'stdout', 'wandb']",,20,,,8,True,False,algorithm-benchmark,False,,,,/home/taufeeque/imitation/output/train_experts/2022-09-05T18:27:27-07:00/seals_ant_1/rollouts/final.pkl,ppo-huggingface,,,imitation.rewards.reward_nets.BasicShapedRewardNet,imitation.util.networks.RunningNorm,imitation.util.networks.RunningNorm,8192,stable_baselines3.ppo.ppo.PPO,16,0.3,3.27750078482474e-06,0.8,0.995,3.249429831179079e-05,0.9,10,0.4351450387648799,50,imitation.policies.base.FeedForward32Policy,imitation.policies.base.NormalizeFeaturesExtractor,imitation.util.networks.RunningNorm,AIRL,seals/Ant-v0,2408.22 ± 665.201 (n=104),-0.402349 ± 19.7147 (n=56) | ||
| ,0,103,False,10000000.0,8192,8192,16,seals/Ant-v0,output/airl/seals_Ant-v0/20221024_082122_47f04c,"['tensorboard', 'stdout', 'wandb']",,20,,,8,True,False,algorithm-benchmark,False,,,,/home/taufeeque/imitation/output/train_experts/2022-09-05T18:27:27-07:00/seals_ant_1/rollouts/final.pkl,ppo-huggingface,,,imitation.rewards.reward_nets.BasicShapedRewardNet,imitation.util.networks.RunningNorm,imitation.util.networks.RunningNorm,8192,stable_baselines3.ppo.ppo.PPO,16,0.3,3.27750078482474e-06,0.8,0.995,3.249429831179079e-05,0.9,10,0.4351450387648799,50,imitation.policies.base.FeedForward32Policy,imitation.policies.base.NormalizeFeaturesExtractor,imitation.util.networks.RunningNorm,AIRL,seals/Ant-v0,2408.22 ± 665.201 (n=104),18.9413 ± 1.1345 (n=56) |
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| agent_path,checkpoint_interval,seed,show_config,total_timesteps,algorithm_kwargs.demo_batch_size,algorithm_kwargs.gen_replay_buffer_capacity,algorithm_kwargs.n_disc_updates_per_round,common.env_name,common.log_dir,common.log_format_strs,common.log_format_strs_additional.wandb,common.log_level,common.log_root,common.max_episode_steps,common.num_vec,common.parallel,common.wandb.wandb_kwargs.monitor_gym,common.wandb.wandb_kwargs.project,common.wandb.wandb_kwargs.save_code,common.wandb.wandb_name_prefix,common.wandb.wandb_tag,demonstrations.n_expert_demos,demonstrations.rollout_path,expert.policy_type,reward.add_std_alpha,reward.ensemble_size,reward.net_cls.py/type,reward.net_kwargs.normalize_input_layer.py/type,reward.normalize_output_layer.py/type,rl.batch_size,rl.rl_cls.py/type,rl.rl_kwargs.batch_size,rl.rl_kwargs.clip_range,rl.rl_kwargs.ent_coef,rl.rl_kwargs.gae_lambda,rl.rl_kwargs.gamma,rl.rl_kwargs.learning_rate,rl.rl_kwargs.max_grad_norm,rl.rl_kwargs.n_epochs,rl.rl_kwargs.vf_coef,train.n_episodes_eval,train.policy_cls.py/type,train.policy_kwargs.features_extractor_class.py/type,train.policy_kwargs.features_extractor_kwargs.normalize_class.py/type,algo,env_name,expert_return_summary,imit_return_summary | ||
| ,0,100,False,10000000.0,2048,512,16,seals/HalfCheetah-v0,output/airl/seals_HalfCheetah-v0/20221021_115006_924cb4,"['tensorboard', 'stdout', 'wandb']",,20,,,8,True,False,algorithm-benchmark,False,,,,/home/taufeeque/imitation/output/train_experts/2022-09-05T18:27:27-07:00/seals_half_cheetah_1/rollouts/final.pkl,ppo-huggingface,,,imitation.rewards.reward_nets.BasicShapedRewardNet,imitation.util.networks.RunningNorm,imitation.util.networks.RunningNorm,8192,stable_baselines3.ppo.ppo.PPO,64,0.1,0.0005544771755195421,0.95,0.95,0.00047248619386801587,0.8,5,0.11483689492120866,50,imitation.policies.base.FeedForward32Policy,imitation.policies.base.NormalizeFeaturesExtractor,imitation.util.networks.RunningNorm,AIRL,seals/HalfCheetah-v0,3465.42 ± 976.462 (n=104),1674.29 ± 581.622 (n=56) | ||
| ,0,104,False,10000000.0,2048,512,16,seals/HalfCheetah-v0,output/airl/seals_HalfCheetah-v0/20221021_115008_b838f5,"['tensorboard', 'stdout', 'wandb']",,20,,,8,True,False,algorithm-benchmark,False,,,,/home/taufeeque/imitation/output/train_experts/2022-09-05T18:27:27-07:00/seals_half_cheetah_1/rollouts/final.pkl,ppo-huggingface,,,imitation.rewards.reward_nets.BasicShapedRewardNet,imitation.util.networks.RunningNorm,imitation.util.networks.RunningNorm,8192,stable_baselines3.ppo.ppo.PPO,64,0.1,0.0005544771755195421,0.95,0.95,0.00047248619386801587,0.8,5,0.11483689492120866,50,imitation.policies.base.FeedForward32Policy,imitation.policies.base.NormalizeFeaturesExtractor,imitation.util.networks.RunningNorm,AIRL,seals/HalfCheetah-v0,3465.42 ± 976.462 (n=104),3652.14 ± 648.766 (n=56) | ||
| ,0,102,False,10000000.0,2048,512,16,seals/HalfCheetah-v0,output/airl/seals_HalfCheetah-v0/20221021_115008_23f6ee,"['tensorboard', 'stdout', 'wandb']",,20,,,8,True,False,algorithm-benchmark,False,,,,/home/taufeeque/imitation/output/train_experts/2022-09-05T18:27:27-07:00/seals_half_cheetah_1/rollouts/final.pkl,ppo-huggingface,,,imitation.rewards.reward_nets.BasicShapedRewardNet,imitation.util.networks.RunningNorm,imitation.util.networks.RunningNorm,8192,stable_baselines3.ppo.ppo.PPO,64,0.1,0.0005544771755195421,0.95,0.95,0.00047248619386801587,0.8,5,0.11483689492120866,50,imitation.policies.base.FeedForward32Policy,imitation.policies.base.NormalizeFeaturesExtractor,imitation.util.networks.RunningNorm,AIRL,seals/HalfCheetah-v0,3465.42 ± 976.462 (n=104),3491.62 ± 368.717 (n=56) | ||
| ,0,101,False,10000000.0,2048,512,16,seals/HalfCheetah-v0,output/airl/seals_HalfCheetah-v0/20221021_115008_ae2f97,"['tensorboard', 'stdout', 'wandb']",,20,,,8,True,False,algorithm-benchmark,False,,,,/home/taufeeque/imitation/output/train_experts/2022-09-05T18:27:27-07:00/seals_half_cheetah_1/rollouts/final.pkl,ppo-huggingface,,,imitation.rewards.reward_nets.BasicShapedRewardNet,imitation.util.networks.RunningNorm,imitation.util.networks.RunningNorm,8192,stable_baselines3.ppo.ppo.PPO,64,0.1,0.0005544771755195421,0.95,0.95,0.00047248619386801587,0.8,5,0.11483689492120866,50,imitation.policies.base.FeedForward32Policy,imitation.policies.base.NormalizeFeaturesExtractor,imitation.util.networks.RunningNorm,AIRL,seals/HalfCheetah-v0,3465.42 ± 976.462 (n=104),4441.25 ± 87.8795 (n=56) | ||
| ,0,103,False,10000000.0,2048,512,16,seals/HalfCheetah-v0,output/airl/seals_HalfCheetah-v0/20221021_115008_1ae278,"['tensorboard', 'stdout', 'wandb']",,20,,,8,True,False,algorithm-benchmark,False,,,,/home/taufeeque/imitation/output/train_experts/2022-09-05T18:27:27-07:00/seals_half_cheetah_1/rollouts/final.pkl,ppo-huggingface,,,imitation.rewards.reward_nets.BasicShapedRewardNet,imitation.util.networks.RunningNorm,imitation.util.networks.RunningNorm,8192,stable_baselines3.ppo.ppo.PPO,64,0.1,0.0005544771755195421,0.95,0.95,0.00047248619386801587,0.8,5,0.11483689492120866,50,imitation.policies.base.FeedForward32Policy,imitation.policies.base.NormalizeFeaturesExtractor,imitation.util.networks.RunningNorm,AIRL,seals/HalfCheetah-v0,3465.42 ± 976.462 (n=104),3960.15 ± 108.134 (n=56) |
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| Original file line number | Diff line number | Diff line change |
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| agent_path,checkpoint_interval,seed,show_config,total_timesteps,algorithm_kwargs.demo_batch_size,algorithm_kwargs.gen_replay_buffer_capacity,algorithm_kwargs.n_disc_updates_per_round,common.env_name,common.log_dir,common.log_format_strs,common.log_format_strs_additional.wandb,common.log_level,common.log_root,common.max_episode_steps,common.num_vec,common.parallel,common.wandb.wandb_kwargs.monitor_gym,common.wandb.wandb_kwargs.project,common.wandb.wandb_kwargs.save_code,common.wandb.wandb_name_prefix,common.wandb.wandb_tag,demonstrations.n_expert_demos,demonstrations.rollout_path,expert.policy_type,reward.add_std_alpha,reward.ensemble_size,reward.net_cls.py/type,reward.net_kwargs.normalize_input_layer.py/type,reward.normalize_output_layer.py/type,rl.batch_size,rl.rl_cls.py/type,rl.rl_kwargs.batch_size,rl.rl_kwargs.clip_range,rl.rl_kwargs.ent_coef,rl.rl_kwargs.gae_lambda,rl.rl_kwargs.gamma,rl.rl_kwargs.learning_rate,rl.rl_kwargs.max_grad_norm,rl.rl_kwargs.n_epochs,rl.rl_kwargs.vf_coef,train.n_episodes_eval,train.policy_cls,train.policy_kwargs.activation_fn.py/type,train.policy_kwargs.features_extractor_class.py/type,train.policy_kwargs.features_extractor_kwargs.normalize_class.py/type,train.policy_kwargs.net_arch,algo,env_name,expert_return_summary,imit_return_summary | ||
| ,0,103,False,10000000.0,2048,8192,16,seals/Hopper-v0,output/airl/seals_Hopper-v0/20221022_223308_a8cbd6,"['tensorboard', 'stdout', 'wandb']",,20,,,8,True,False,algorithm-benchmark,False,,,,/home/taufeeque/imitation/output/train_experts/2022-10-11T06:27:42-07:00/seals_hopper_2/rollouts/final.pkl,ppo-huggingface,,,imitation.rewards.reward_nets.BasicShapedRewardNet,imitation.util.networks.RunningNorm,imitation.util.networks.RunningNorm,8192,stable_baselines3.ppo.ppo.PPO,512,0.1,0.009709494745755033,0.98,0.995,0.0005807211840258373,0.9,20,0.20315938606555833,50,MlpPolicy,torch.nn.modules.activation.ReLU,imitation.policies.base.NormalizeFeaturesExtractor,imitation.util.networks.RunningNorm,"[{'pi': [64, 64], 'vf': [64, 64]}]",AIRL,seals/Hopper-v0,2630.92 ± 112.582 (n=104),2600.12 ± 155.143 (n=56) | ||
| ,0,101,False,10000000.0,2048,8192,16,seals/Hopper-v0,output/airl/seals_Hopper-v0/20221022_223308_299f28,"['tensorboard', 'stdout', 'wandb']",,20,,,8,True,False,algorithm-benchmark,False,,,,/home/taufeeque/imitation/output/train_experts/2022-10-11T06:27:42-07:00/seals_hopper_2/rollouts/final.pkl,ppo-huggingface,,,imitation.rewards.reward_nets.BasicShapedRewardNet,imitation.util.networks.RunningNorm,imitation.util.networks.RunningNorm,8192,stable_baselines3.ppo.ppo.PPO,512,0.1,0.009709494745755033,0.98,0.995,0.0005807211840258373,0.9,20,0.20315938606555833,50,MlpPolicy,torch.nn.modules.activation.ReLU,imitation.policies.base.NormalizeFeaturesExtractor,imitation.util.networks.RunningNorm,"[{'pi': [64, 64], 'vf': [64, 64]}]",AIRL,seals/Hopper-v0,2630.92 ± 112.582 (n=104),2663.1 ± 121.83 (n=56) | ||
| ,0,104,False,10000000.0,2048,8192,16,seals/Hopper-v0,output/airl/seals_Hopper-v0/20221022_223307_1607e3,"['tensorboard', 'stdout', 'wandb']",,20,,,8,True,False,algorithm-benchmark,False,,,,/home/taufeeque/imitation/output/train_experts/2022-10-11T06:27:42-07:00/seals_hopper_2/rollouts/final.pkl,ppo-huggingface,,,imitation.rewards.reward_nets.BasicShapedRewardNet,imitation.util.networks.RunningNorm,imitation.util.networks.RunningNorm,8192,stable_baselines3.ppo.ppo.PPO,512,0.1,0.009709494745755033,0.98,0.995,0.0005807211840258373,0.9,20,0.20315938606555833,50,MlpPolicy,torch.nn.modules.activation.ReLU,imitation.policies.base.NormalizeFeaturesExtractor,imitation.util.networks.RunningNorm,"[{'pi': [64, 64], 'vf': [64, 64]}]",AIRL,seals/Hopper-v0,2630.92 ± 112.582 (n=104),2740.77 ± 107.306 (n=56) | ||
| ,0,100,False,10000000.0,2048,8192,16,seals/Hopper-v0,output/airl/seals_Hopper-v0/20221022_223305_7116b9,"['tensorboard', 'stdout', 'wandb']",,20,,,8,True,False,algorithm-benchmark,False,,,,/home/taufeeque/imitation/output/train_experts/2022-10-11T06:27:42-07:00/seals_hopper_2/rollouts/final.pkl,ppo-huggingface,,,imitation.rewards.reward_nets.BasicShapedRewardNet,imitation.util.networks.RunningNorm,imitation.util.networks.RunningNorm,8192,stable_baselines3.ppo.ppo.PPO,512,0.1,0.009709494745755033,0.98,0.995,0.0005807211840258373,0.9,20,0.20315938606555833,50,MlpPolicy,torch.nn.modules.activation.ReLU,imitation.policies.base.NormalizeFeaturesExtractor,imitation.util.networks.RunningNorm,"[{'pi': [64, 64], 'vf': [64, 64]}]",AIRL,seals/Hopper-v0,2630.92 ± 112.582 (n=104),2758.67 ± 121.298 (n=56) | ||
| ,0,102,False,10000000.0,2048,8192,16,seals/Hopper-v0,output/airl/seals_Hopper-v0/20221022_223307_23fde3,"['tensorboard', 'stdout', 'wandb']",,20,,,8,True,False,algorithm-benchmark,False,,,,/home/taufeeque/imitation/output/train_experts/2022-10-11T06:27:42-07:00/seals_hopper_2/rollouts/final.pkl,ppo-huggingface,,,imitation.rewards.reward_nets.BasicShapedRewardNet,imitation.util.networks.RunningNorm,imitation.util.networks.RunningNorm,8192,stable_baselines3.ppo.ppo.PPO,512,0.1,0.009709494745755033,0.98,0.995,0.0005807211840258373,0.9,20,0.20315938606555833,50,MlpPolicy,torch.nn.modules.activation.ReLU,imitation.policies.base.NormalizeFeaturesExtractor,imitation.util.networks.RunningNorm,"[{'pi': [64, 64], 'vf': [64, 64]}]",AIRL,seals/Hopper-v0,2630.92 ± 112.582 (n=104),2613.26 ± 128.037 (n=56) |
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Should this be
--name <name>or--name=<name>?