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README.md +12 -4
args.yml +11 -5
config.yml +5 -1
replay.mp4 +2 -2
results.json +1 -1
sac-seals-Ant-v0.zip +2 -2
sac-seals-Ant-v0/_stable_baselines3_version +1 -1
sac-seals-Ant-v0/actor.optimizer.pth +1 -1
sac-seals-Ant-v0/critic.optimizer.pth +1 -1
sac-seals-Ant-v0/data +27 -25
sac-seals-Ant-v0/ent_coef_optimizer.pth +2 -2
sac-seals-Ant-v0/policy.pth +1 -1
sac-seals-Ant-v0/pytorch_variables.pth +1 -1
sac-seals-Ant-v0/system_info.txt +2 -2
train_eval_metrics.zip +2 -2

README.md CHANGED Viewed

@@ -10,7 +10,7 @@ model-index:
   results:
   - metrics:
     - type: mean_reward
-      value: 1252.31 +/- 101.70
       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/Ant-v0 -orga HumanCompatibleAI -f logs/
 python enjoy.py --algo sac --env seals/Ant-v0  -f logs/
 ```
 ## Training (with the RL Zoo)
 ```
 python train.py --algo sac --env seals/Ant-v0 -f logs/
 # Upload the model and generate video (when possible)
-python -m utils.push_to_hub --algo sac --env seals/Ant-v0 -f logs/ -orga HumanCompatibleAI
 ```
 ## Hyperparameters
@@ -58,7 +64,9 @@ OrderedDict([('batch_size', 512),
              ('n_timesteps', 1000000.0),
              ('policy', 'MlpPolicy'),
              ('policy_kwargs',
-              'dict(net_arch=[256, 256], log_std_init=-2.2692589009754176)'),
              ('tau', 0.05),
              ('train_freq', 64),
              ('normalize', False)])

   results:
   - metrics:
     - type: mean_reward
+      value: 1057.04 +/- 13.50
       name: mean_reward
     task:
       type: reinforcement-learning
 ```
 # Download model and save it into the logs/ folder
+python -m rl_zoo3.load_from_hub --algo sac --env seals/Ant-v0 -orga HumanCompatibleAI -f logs/
 python enjoy.py --algo sac --env seals/Ant-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/Ant-v0 -orga HumanCompatibleAI -f logs/
+rl_zoo3 enjoy --algo sac --env seals/Ant-v0  -f logs/
+```
 ## Training (with the RL Zoo)
 ```
 python train.py --algo sac --env seals/Ant-v0 -f logs/
 # Upload the model and generate video (when possible)
+python -m rl_zoo3.push_to_hub --algo sac --env seals/Ant-v0 -f logs/ -orga HumanCompatibleAI
 ```
 ## Hyperparameters
              ('n_timesteps', 1000000.0),
              ('policy', 'MlpPolicy'),
              ('policy_kwargs',
+              {'log_std_init': -2.2692589009754176,
+               'net_arch': [256, 256],
+               'use_sde': False}),
              ('tau', 0.05),
              ('train_freq', 64),
              ('normalize', False)])

args.yml CHANGED Viewed

@@ -1,6 +1,8 @@
 !!python/object/apply:collections.OrderedDict
 - - - algo
     - sac
   - - device
     - cpu
   - - env
@@ -16,7 +18,7 @@
   - - hyperparams
     - null
   - - log_folder
-    - seals_experts_wandb_oldpickle/seed_10/
   - - log_interval
     - -1
   - - max_total_trials
@@ -41,6 +43,8 @@
     - null
   - - optimize_hyperparameters
     - false
   - - pruner
     - median
   - - sampler
@@ -50,13 +54,13 @@
   - - save_replay_buffer
     - false
   - - seed
-    - 10
   - - storage
     - null
   - - study_name
     - null
   - - tensorboard_log
-    - runs/seals/Ant-v0__sac__10__1658840186
   - - track
     - true
   - - trained_agent
@@ -70,6 +74,8 @@
   - - verbose
     - 1
   - - wandb_entity
-    - null
   - - wandb_project_name
-    - seals-experts-oldpickle

 !!python/object/apply:collections.OrderedDict
 - - - algo
     - sac
+  - - conf_file
+    - hyperparams/python/sac.py
   - - device
     - cpu
   - - env
   - - hyperparams
     - null
   - - log_folder
+    - logs
   - - log_interval
     - -1
   - - max_total_trials
     - null
   - - optimize_hyperparameters
     - false
+  - - progress
+    - false
   - - pruner
     - median
   - - sampler
   - - save_replay_buffer
     - false
   - - seed
+    - 3
   - - storage
     - null
   - - study_name
     - null
   - - tensorboard_log
+    - runs/seals/Ant-v0__sac__3__1670517964
   - - track
     - true
   - - trained_agent
   - - verbose
     - 1
   - - wandb_entity
+    - ernestum
   - - wandb_project_name
+    - seals-experts-normalized
+  - - yaml_file
+    - null

config.yml CHANGED Viewed

@@ -14,7 +14,11 @@
   - - policy
     - MlpPolicy
   - - policy_kwargs
-    - dict(net_arch=[256, 256], log_std_init=-2.2692589009754176)
   - - tau
     - 0.05
   - - train_freq

   - - policy
     - MlpPolicy
   - - policy_kwargs
+    - log_std_init: -2.2692589009754176
+      net_arch:
+      - 256
+      - 256
+      use_sde: false
   - - tau
     - 0.05
   - - train_freq

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	@@ -1 +1 @@
1	- {"mean_reward": ~~1252~~.~~3085881000002~~, "std_reward": ~~101~~.~~70365982356354~~, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-07-~~27T17~~:11:26.~~174904~~"}


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         "__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    ",
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-        "reset_noise": "<function SACPolicy.reset_noise at 0x7fdacc23b280>",
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     "_last_episode_starts": {
@@ -74,7 +74,7 @@
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@@ -82,7 +82,7 @@
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@@ -100,13 +100,13 @@
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         "__module__": "stable_baselines3.common.buffers",
-        "__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:\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 0x7fdacc288280>",
-        "add": "<function ReplayBuffer.add at 0x7fdacc288310>",
-        "sample": "<function ReplayBuffer.sample at 0x7fdacc2883a0>",
-        "_get_samples": "<function ReplayBuffer._get_samples at 0x7fdacc288430>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc_data object at 0x7fdacc30d480>"
     },
     "replay_buffer_kwargs": {},
     "train_freq": {
@@ -116,5 +116,7 @@
     "use_sde_at_warmup": false,
     "target_entropy": -8.0,
     "ent_coef": "auto",
-    "target_update_interval": 1
 }

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         "__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 0x7f75a4406ee0>",
+        "_build": "<function SACPolicy._build at 0x7f75a4406f70>",
+        "_get_constructor_parameters": "<function SACPolicy._get_constructor_parameters at 0x7f75a438f040>",
+        "reset_noise": "<function SACPolicy.reset_noise at 0x7f75a438f0d0>",
+        "make_actor": "<function SACPolicy.make_actor at 0x7f75a438f160>",
+        "make_critic": "<function SACPolicy.make_critic at 0x7f75a438f1f0>",
+        "forward": "<function SACPolicy.forward at 0x7f75a438f280>",
+        "_predict": "<function SACPolicy._predict at 0x7f75a438f310>",
+        "set_training_mode": "<function SACPolicy.set_training_mode at 0x7f75a438f3a0>",
         "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f75a4404c90>"
     },
     "verbose": 1,
     "policy_kwargs": {
     },
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         "__module__": "stable_baselines3.common.buffers",
+        "__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    ",
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         "__abstractmethods__": "frozenset()",
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     },
     "replay_buffer_kwargs": {},
     "train_freq": {
     "use_sde_at_warmup": false,
     "target_entropy": -8.0,
     "ent_coef": "auto",
+    "target_update_interval": 1,
+    "batch_norm_stats": [],
+    "batch_norm_stats_target": []
 }

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-Stable-Baselines3: 1.6.0
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 Numpy: 1.22.3

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