Initial commit

.gitattributes

@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.mp4 filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,75 @@
+---
+library_name: stable-baselines3
+tags:
+- Hopper-v3
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: TD3
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: Hopper-v3
+      type: Hopper-v3
+    metrics:
+    - type: mean_reward
+      value: 1708.59 +/- 1293.86
+      name: mean_reward
+      verified: false
+---
+
+# **TD3** Agent playing **Hopper-v3**
+This is a trained model of a **TD3** agent playing **Hopper-v3**
+using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
+and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
+
+The RL Zoo is a training framework for Stable Baselines3
+reinforcement learning agents,
+with hyperparameter optimization and pre-trained agents included.
+
+## Usage (with SB3 RL Zoo)
+
+RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
+SB3: https://github.com/DLR-RM/stable-baselines3<br/>
+SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
+
+Install the RL Zoo (with SB3 and SB3-Contrib):
+```bash
+pip install rl_zoo3
+```
+
+```
+# Download model and save it into the logs/ folder
+python -m rl_zoo3.load_from_hub --algo td3 --env Hopper-v3 -orga qgallouedec -f logs/
+python -m rl_zoo3.enjoy --algo td3 --env Hopper-v3  -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 td3 --env Hopper-v3 -orga qgallouedec -f logs/
+python -m rl_zoo3.enjoy --algo td3 --env Hopper-v3  -f logs/
+```
+
+## Training (with the RL Zoo)
+```
+python -m rl_zoo3.train --algo td3 --env Hopper-v3 -f logs/
+# Upload the model and generate video (when possible)
+python -m rl_zoo3.push_to_hub --algo td3 --env Hopper-v3 -f logs/ -orga qgallouedec
+```
+
+## Hyperparameters
+```python
+OrderedDict([('batch_size', 256),
+             ('gradient_steps', 1),
+             ('learning_rate', 0.0003),
+             ('learning_starts', 10000),
+             ('n_timesteps', 1000000.0),
+             ('noise_std', 0.1),
+             ('noise_type', 'normal'),
+             ('policy', 'MlpPolicy'),
+             ('train_freq', 1),
+             ('normalize', False)])
+```

args.yml

@@ -0,0 +1,83 @@
+!!python/object/apply:collections.OrderedDict
+- - - algo
+    - td3
+  - - conf_file
+    - null
+  - - device
+    - auto
+  - - env
+    - Hopper-v3
+  - - env_kwargs
+    - null
+  - - eval_episodes
+    - 20
+  - - eval_freq
+    - 25000
+  - - gym_packages
+    - []
+  - - hyperparams
+    - null
+  - - log_folder
+    - logs
+  - - log_interval
+    - -1
+  - - max_total_trials
+    - null
+  - - n_eval_envs
+    - 5
+  - - n_evaluations
+    - null
+  - - n_jobs
+    - 1
+  - - n_startup_trials
+    - 10
+  - - n_timesteps
+    - -1
+  - - n_trials
+    - 500
+  - - no_optim_plots
+    - false
+  - - num_threads
+    - -1
+  - - optimization_log_path
+    - null
+  - - optimize_hyperparameters
+    - false
+  - - progress
+    - false
+  - - pruner
+    - median
+  - - sampler
+    - tpe
+  - - save_freq
+    - -1
+  - - save_replay_buffer
+    - false
+  - - seed
+    - 3636180001
+  - - storage
+    - null
+  - - study_name
+    - null
+  - - tensorboard_log
+    - runs/Hopper-v3__td3__3636180001__1676740791
+  - - track
+    - true
+  - - trained_agent
+    - ''
+  - - truncate_last_trajectory
+    - true
+  - - uuid
+    - false
+  - - vec_env
+    - dummy
+  - - verbose
+    - 1
+  - - wandb_entity
+    - openrlbenchmark
+  - - wandb_project_name
+    - sb3
+  - - wandb_tags
+    - []
+  - - yaml_file
+    - null

config.yml

@@ -0,0 +1,19 @@
+!!python/object/apply:collections.OrderedDict
+- - - batch_size
+    - 256
+  - - gradient_steps
+    - 1
+  - - learning_rate
+    - 0.0003
+  - - learning_starts
+    - 10000
+  - - n_timesteps
+    - 1000000.0
+  - - noise_std
+    - 0.1
+  - - noise_type
+    - normal
+  - - policy
+    - MlpPolicy
+  - - train_freq
+    - 1

env_kwargs.yml

@@ -0,0 +1 @@
+{}

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5fd5ce14e86e8a1ea69d17a87835ddbd5da423744be8304c29982c0d879b635e
+size 1387023

results.json

@@ -0,0 +1 @@
+{"mean_reward": 1708.5913813, "std_reward": 1293.862610478071, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-28T17:29:50.381578"}

td3-Hopper-v3.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1e3e56d5bd1be11d0ecd99a927289098900635f966df9b2e08515fb25fe296c6
+size 6115041

td3-Hopper-v3/_stable_baselines3_version

@@ -0,0 +1 @@
+1.8.0a6

td3-Hopper-v3/actor.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5ed8a5181b18f55b3e377fdd575cd5734931f54eef3cee7bc354503283c1cb8e
+size 1012911

td3-Hopper-v3/critic.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bb8beb7104589d6eea4bee45361de144cd0d15b03d8e409870adc385db14d942
+size 2035001

td3-Hopper-v3/data

@@ -0,0 +1,121 @@
+{
+    "policy_class": {
+        ":type:": "<class 'abc.ABCMeta'>",
+        ":serialized:": "gAWVMAAAAAAAAACMHnN0YWJsZV9iYXNlbGluZXMzLnRkMy5wb2xpY2llc5SMCVREM1BvbGljeZSTlC4=",
+        "__module__": "stable_baselines3.td3.policies",
+        "__doc__": "\n    Policy class (with both actor and critic) for TD3.\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 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 TD3Policy.__init__ at 0x7f21b41f0af0>",
+        "_build": "<function TD3Policy._build at 0x7f21b41f0b80>",
+        "_get_constructor_parameters": "<function TD3Policy._get_constructor_parameters at 0x7f21b41f0c10>",
+        "make_actor": "<function TD3Policy.make_actor at 0x7f21b41f0ca0>",
+        "make_critic": "<function TD3Policy.make_critic at 0x7f21b41f0d30>",
+        "forward": "<function TD3Policy.forward at 0x7f21b41f0dc0>",
+        "_predict": "<function TD3Policy._predict at 0x7f21b41f0e50>",
+        "set_training_mode": "<function TD3Policy.set_training_mode at 0x7f21b41f0ee0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f21b41f47c0>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "observation_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float64",
+        "_shape": [
+            11
+        ],
+        "low": "[-inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf]",
+        "high": "[inf inf inf inf inf inf inf inf inf inf inf]",
+        "bounded_below": "[False False False False False False False False False False False]",
+        "bounded_above": "[False False False False False False False False False False False]",
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "_shape": [
+            3
+        ],
+        "low": "[-1. -1. -1.]",
+        "high": "[1. 1. 1.]",
+        "bounded_below": "[ True  True  True]",
+        "bounded_above": "[ True  True  True]",
+        "_np_random": "RandomState(MT19937)"
+    },
+    "n_envs": 1,
+    "num_timesteps": 1000000,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": 0,
+    "action_noise": {
+        ":type:": "<class 'stable_baselines3.common.noise.NormalActionNoise'>",
+        ":serialized:": "gAWVCgEAAAAAAACMHnN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5ub2lzZZSMEU5vcm1hbEFjdGlvbk5vaXNllJOUKYGUfZQojANfbXWUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWGAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJmOJSJiIeUUpQoSwOMATyUTk5OSv////9K/////0sAdJRiSwOFlIwBQ5R0lFKUjAZfc2lnbWGUaAgolhgAAAAAAAAAmpmZmZmZuT+amZmZmZm5P5qZmZmZmbk/lGgPSwOFlGgTdJRSlHViLg==",
+        "_mu": "[0. 0. 0.]",
+        "_sigma": "[0.1 0.1 0.1]"
+    },
+    "start_time": 1676740794033456913,
+    "learning_rate": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "tensorboard_log": "runs/Hopper-v3__td3__3636180001__1676740791/Hopper-v3",
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": null,
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAAGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="
+    },
+    "_last_original_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVzQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJZYAAAAAAAAANzj93c3GvM/Q4lBj13xxL+vNNRojNhlPzued0CO4Ou/mYqPK5jZ6T/A4JhK9q8BQFlUrG5xVgDAEueJ+CNk4L+YE+Kz80aYv7G7UPcTnvA/0dHx5euF5r+UjAVudW1weZSMBWR0eXBllJOUjAJmOJSJiIeUUpQoSwOMATyUTk5OSv////9K/////0sAdJRiSwFLC4aUjAFDlHSUUpQu"
+    },
+    "_episode_num": 3503,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": 0.0,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 990000,
+    "buffer_size": 1,
+    "batch_size": 256,
+    "learning_starts": 10000,
+    "tau": 0.005,
+    "gamma": 0.99,
+    "gradient_steps": 1,
+    "optimize_memory_usage": false,
+    "replay_buffer_class": {
+        ":type:": "<class 'abc.ABCMeta'>",
+        ":serialized:": "gAWVNQAAAAAAAACMIHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5idWZmZXJzlIwMUmVwbGF5QnVmZmVylJOULg==",
+        "__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    ",
+        "__init__": "<function ReplayBuffer.__init__ at 0x7f21b41ee5e0>",
+        "add": "<function ReplayBuffer.add at 0x7f21b41ee670>",
+        "sample": "<function ReplayBuffer.sample at 0x7f21b41ee700>",
+        "_get_samples": "<function ReplayBuffer._get_samples at 0x7f21b41ee790>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f21b47cb100>"
+    },
+    "replay_buffer_kwargs": {},
+    "train_freq": {
+        ":type:": "<class 'stable_baselines3.common.type_aliases.TrainFreq'>",
+        ":serialized:": "gAWVYQAAAAAAAACMJXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi50eXBlX2FsaWFzZXOUjAlUcmFpbkZyZXGUk5RLAWgAjBJUcmFpbkZyZXF1ZW5jeVVuaXSUk5SMBHN0ZXCUhZRSlIaUgZQu"
+    },
+    "use_sde_at_warmup": false,
+    "policy_delay": 2,
+    "target_noise_clip": 0.5,
+    "target_policy_noise": 0.2,
+    "actor_batch_norm_stats": [],
+    "critic_batch_norm_stats": [],
+    "actor_batch_norm_stats_target": [],
+    "critic_batch_norm_stats_target": []
+}

td3-Hopper-v3/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b12e71b3b09c99407cc52751d25213b5655c1cad35cb29aba72c8861bc2da013
+size 3045753

td3-Hopper-v3/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
+size 431

td3-Hopper-v3/system_info.txt

@@ -0,0 +1,7 @@
+- OS: Linux-5.19.0-32-generic-x86_64-with-glibc2.35 # 33~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Mon Jan 30 17:03:34 UTC 2
+- Python: 3.9.12
+- Stable-Baselines3: 1.8.0a6
+- PyTorch: 1.13.1+cu117
+- GPU Enabled: True
+- Numpy: 1.24.1
+- Gym: 0.21.0

train_eval_metrics.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2da0655dfafcc9809f2a4bdfd60508136f60bcf4fde4f2a3df9400a44baa834a
+size 107722