Initial commit

README.md

@@ -10,7 +10,7 @@ model-index:
   results:
   - metrics:
     - type: mean_reward
-      value: 35.88 +/- 2.02
+      value: 28.16 +/- 0.72
       name: mean_reward
     task:
       type: reinforcement-learning
@@ -37,15 +37,21 @@ SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
 
 ```
 # Download model and save it into the logs/ folder
-python -m utils.load_from_hub --algo sac --env seals/Swimmer-v0 -orga HumanCompatibleAI -f logs/
+python -m rl_zoo3.load_from_hub --algo sac --env seals/Swimmer-v0 -orga HumanCompatibleAI -f logs/
 python enjoy.py --algo sac --env seals/Swimmer-v0  -f logs/
 ```
 
+If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
+```
+python -m rl_zoo3.load_from_hub --algo sac --env seals/Swimmer-v0 -orga HumanCompatibleAI -f logs/
+rl_zoo3 enjoy --algo sac --env seals/Swimmer-v0  -f logs/
+```
+
 ## Training (with the RL Zoo)
 ```
 python train.py --algo sac --env seals/Swimmer-v0 -f logs/
 # Upload the model and generate video (when possible)
-python -m utils.push_to_hub --algo sac --env seals/Swimmer-v0 -f logs/ -orga HumanCompatibleAI
+python -m rl_zoo3.push_to_hub --algo sac --env seals/Swimmer-v0 -f logs/ -orga HumanCompatibleAI
 ```
 
 ## Hyperparameters
@@ -58,7 +64,9 @@ OrderedDict([('batch_size', 128),
              ('n_timesteps', 1000000.0),
              ('policy', 'MlpPolicy'),
              ('policy_kwargs',
-              'dict(net_arch=[400, 300], log_std_init=-2.689958330139309)'),
+              {'log_std_init': -2.689958330139309,
+               'net_arch': [400, 300],
+               'use_sde': False}),
              ('tau', 0.01),
              ('train_freq', 256),
              ('normalize', False)])

args.yml

@@ -1,6 +1,8 @@
 !!python/object/apply:collections.OrderedDict
 - - - algo
     - sac
+  - - conf_file
+    - hyperparams/python/sac.py
   - - device
     - cpu
   - - env
@@ -16,7 +18,7 @@
   - - hyperparams
     - null
   - - log_folder
-    - seals_experts_wandb_oldpickle/seed_5/
+    - logs
   - - log_interval
     - -1
   - - max_total_trials
@@ -41,6 +43,8 @@
     - null
   - - optimize_hyperparameters
     - false
+  - - progress
+    - false
   - - pruner
     - median
   - - sampler
@@ -50,13 +54,13 @@
   - - save_replay_buffer
     - false
   - - seed
-    - 5
+    - 7
   - - storage
     - null
   - - study_name
     - null
   - - tensorboard_log
-    - runs/seals/Swimmer-v0__sac__5__1658841516
+    - runs/seals/Swimmer-v0__sac__7__1670518603
   - - track
     - true
   - - trained_agent
@@ -70,6 +74,8 @@
   - - verbose
     - 1
   - - wandb_entity
-    - null
+    - ernestum
   - - wandb_project_name
-    - seals-experts-oldpickle
+    - seals-experts-normalized
+  - - yaml_file
+    - null

config.yml

@@ -14,7 +14,11 @@
   - - policy
     - MlpPolicy
   - - policy_kwargs
-    - dict(net_arch=[400, 300], log_std_init=-2.689958330139309)
+    - log_std_init: -2.689958330139309
+      net_arch:
+      - 400
+      - 300
+      use_sde: false
   - - tau
     - 0.01
   - - train_freq

replay.mp4

@@ -1,3 +1,3 @@
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-size 425474
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+size 448267

results.json

@@ -1 +1 @@
-{"mean_reward": 35.87843099999999, "std_reward": 2.0229172814859733, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-07-27T17:13:28.059429"}
+{"mean_reward": 28.160976400000003, "std_reward": 0.7219696332704303, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-31T18:33:54.752461"}

sac-seals-Swimmer-v0.zip

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+oid sha256:f62ba56e2bc5ed9a84b4b0726ae3b762dd0f073421dae062d62b623030db0670
+size 5585156

sac-seals-Swimmer-v0/_stable_baselines3_version

@@ -1 +1 @@
-1.6.0
+1.6.2

sac-seals-Swimmer-v0/actor.optimizer.pth

@@ -1,3 +1,3 @@
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 size 1013341

sac-seals-Swimmer-v0/critic.optimizer.pth

@@ -1,3 +1,3 @@
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-oid sha256:30116f753680549c64eb86f0d60e497bc07d4562924acd054cb97fabae90a91b
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 size 2021689

sac-seals-Swimmer-v0/data

@@ -4,17 +4,17 @@
         ":serialized:": "gAWVMAAAAAAAAACMHnN0YWJsZV9iYXNlbGluZXMzLnNhYy5wb2xpY2llc5SMCVNBQ1BvbGljeZSTlC4=",
         "__module__": "stable_baselines3.sac.policies",
         "__doc__": "\n    Policy class (with both actor and critic) for SAC.\n\n    :param observation_space: Observation space\n    :param action_space: Action space\n    :param lr_schedule: Learning rate schedule (could be constant)\n    :param net_arch: The specification of the policy and value networks.\n    :param activation_fn: Activation function\n    :param use_sde: Whether to use State Dependent Exploration or not\n    :param log_std_init: Initial value for the log standard deviation\n    :param sde_net_arch: Network architecture for extracting features\n        when using gSDE. If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\n    :param use_expln: Use ``expln()`` function instead of ``exp()`` when using gSDE to ensure\n        a positive standard deviation (cf paper). It allows to keep variance\n        above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n    :param clip_mean: Clip the mean output when using gSDE to avoid numerical instability.\n    :param features_extractor_class: Features extractor to use.\n    :param features_extractor_kwargs: Keyword arguments\n        to pass to the features extractor.\n    :param normalize_images: Whether to normalize images or not,\n         dividing by 255.0 (True by default)\n    :param optimizer_class: The optimizer to use,\n        ``th.optim.Adam`` by default\n    :param optimizer_kwargs: Additional keyword arguments,\n        excluding the learning rate, to pass to the optimizer\n    :param n_critics: Number of critic networks to create.\n    :param share_features_extractor: Whether to share or not the features extractor\n        between the actor and the critic (this saves computation time)\n    ",
-        "__init__": "<function SACPolicy.__init__ at 0x7f564e7b30d0>",
-        "_build": "<function SACPolicy._build at 0x7f564e7b3160>",
-        "_get_constructor_parameters": "<function SACPolicy._get_constructor_parameters at 0x7f564e7b31f0>",
-        "reset_noise": "<function SACPolicy.reset_noise at 0x7f564e7b3280>",
-        "make_actor": "<function SACPolicy.make_actor at 0x7f564e7b3310>",
-        "make_critic": "<function SACPolicy.make_critic at 0x7f564e7b33a0>",
-        "forward": "<function SACPolicy.forward at 0x7f564e7b3430>",
-        "_predict": "<function SACPolicy._predict at 0x7f564e7b34c0>",
-        "set_training_mode": "<function SACPolicy.set_training_mode at 0x7f564e7b3550>",
+        "__init__": "<function SACPolicy.__init__ at 0x7f73fdab1ee0>",
+        "_build": "<function SACPolicy._build at 0x7f73fdab1f70>",
+        "_get_constructor_parameters": "<function SACPolicy._get_constructor_parameters at 0x7f73fda3a040>",
+        "reset_noise": "<function SACPolicy.reset_noise at 0x7f73fda3a0d0>",
+        "make_actor": "<function SACPolicy.make_actor at 0x7f73fda3a160>",
+        "make_critic": "<function SACPolicy.make_critic at 0x7f73fda3a1f0>",
+        "forward": "<function SACPolicy.forward at 0x7f73fda3a280>",
+        "_predict": "<function SACPolicy._predict at 0x7f73fda3a310>",
+        "set_training_mode": "<function SACPolicy.set_training_mode at 0x7f73fda3a3a0>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc_data object at 0x7f564e7ac900>"
+        "_abc_impl": "<_abc_data object at 0x7f73fdab0cc0>"
     },
     "verbose": 1,
     "policy_kwargs": {
@@ -40,7 +40,7 @@
     },
     "action_space": {
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     "_last_obs": null,
     "_last_episode_starts": {
@@ -74,7 +74,7 @@
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     "_last_original_obs": {
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     "use_sde": false,
@@ -82,7 +82,7 @@
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         "__module__": "stable_baselines3.common.buffers",
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-        "add": "<function ReplayBuffer.add at 0x7f564e801310>",
-        "sample": "<function ReplayBuffer.sample at 0x7f564e8013a0>",
-        "_get_samples": "<function ReplayBuffer._get_samples at 0x7f564e801430>",
+        "__doc__": "\n    Replay buffer used in off-policy algorithms like SAC/TD3.\n\n    :param buffer_size: Max number of element in the buffer\n    :param observation_space: Observation space\n    :param action_space: Action space\n    :param device: PyTorch device\n    :param n_envs: Number of parallel environments\n    :param optimize_memory_usage: Enable a memory efficient variant\n        of the replay buffer which reduces by almost a factor two the memory used,\n        at a cost of more complexity.\n        See https://github.com/DLR-RM/stable-baselines3/issues/37#issuecomment-637501195\n        and https://github.com/DLR-RM/stable-baselines3/pull/28#issuecomment-637559274\n        Cannot be used in combination with handle_timeout_termination.\n    :param handle_timeout_termination: Handle timeout termination (due to timelimit)\n        separately and treat the task as infinite horizon task.\n        https://github.com/DLR-RM/stable-baselines3/issues/284\n    ",
+        "__init__": "<function ReplayBuffer.__init__ at 0x7f73fda860d0>",
+        "add": "<function ReplayBuffer.add at 0x7f73fda86160>",
+        "sample": "<function ReplayBuffer.sample at 0x7f73fda861f0>",
+        "_get_samples": "<function ReplayBuffer._get_samples at 0x7f73fda86280>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc_data object at 0x7f564e885480>"
+        "_abc_impl": "<_abc_data object at 0x7f73fdb08300>"
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     "replay_buffer_kwargs": {},
     "train_freq": {
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     "use_sde_at_warmup": false,
     "target_entropy": -2.0,
     "ent_coef": "auto",
-    "target_update_interval": 1
+    "target_update_interval": 1,
+    "batch_norm_stats": [],
+    "batch_norm_stats_target": []
 }

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sac-seals-Swimmer-v0/system_info.txt

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-OS: Linux-5.4.0-122-generic-x86_64-with-glibc2.29 #138-Ubuntu SMP Wed Jun 22 15:00:31 UTC 2022
+OS: Linux-5.4.0-125-generic-x86_64-with-glibc2.29 #141-Ubuntu SMP Wed Aug 10 13:42:03 UTC 2022
 Python: 3.8.10
-Stable-Baselines3: 1.6.0
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 PyTorch: 1.11.0+cu102
 GPU Enabled: False
 Numpy: 1.22.3

train_eval_metrics.zip

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