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 +1,79 @@
+---
+library_name: stable-baselines3
+tags:
+- Ant-v3
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: TRPO
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: Ant-v3
+      type: Ant-v3
+    metrics:
+    - type: mean_reward
+      value: 3481.59 +/- 108.68
+      name: mean_reward
+      verified: false
+---
+
+# **TRPO** Agent playing **Ant-v3**
+This is a trained model of a **TRPO** agent playing **Ant-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 trpo --env Ant-v3 -orga qgallouedec -f logs/
+python -m rl_zoo3.enjoy --algo trpo --env Ant-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 trpo --env Ant-v3 -orga qgallouedec -f logs/
+python -m rl_zoo3.enjoy --algo trpo --env Ant-v3  -f logs/
+```
+
+## Training (with the RL Zoo)
+```
+python -m rl_zoo3.train --algo trpo --env Ant-v3 -f logs/
+# Upload the model and generate video (when possible)
+python -m rl_zoo3.push_to_hub --algo trpo --env Ant-v3 -f logs/ -orga qgallouedec
+```
+
+## Hyperparameters
+```python
+OrderedDict([('batch_size', 128),
+             ('cg_damping', 0.1),
+             ('cg_max_steps', 25),
+             ('gae_lambda', 0.95),
+             ('gamma', 0.99),
+             ('learning_rate', 0.001),
+             ('n_critic_updates', 20),
+             ('n_envs', 2),
+             ('n_steps', 1024),
+             ('n_timesteps', 1000000.0),
+             ('normalize', True),
+             ('policy', 'MlpPolicy'),
+             ('sub_sampling_factor', 1),
+             ('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])
+```

args.yml

@@ -0,0 +1,83 @@
+!!python/object/apply:collections.OrderedDict
+- - - algo
+    - trpo
+  - - conf_file
+    - null
+  - - device
+    - auto
+  - - env
+    - Ant-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
+    - 2229209209
+  - - storage
+    - null
+  - - study_name
+    - null
+  - - tensorboard_log
+    - runs/Ant-v3__trpo__2229209209__1676304987
+  - - 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 +1,27 @@
+!!python/object/apply:collections.OrderedDict
+- - - batch_size
+    - 128
+  - - cg_damping
+    - 0.1
+  - - cg_max_steps
+    - 25
+  - - gae_lambda
+    - 0.95
+  - - gamma
+    - 0.99
+  - - learning_rate
+    - 0.001
+  - - n_critic_updates
+    - 20
+  - - n_envs
+    - 2
+  - - n_steps
+    - 1024
+  - - n_timesteps
+    - 1000000.0
+  - - normalize
+    - true
+  - - policy
+    - MlpPolicy
+  - - sub_sampling_factor
+    - 1

env_kwargs.yml

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

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6aa9da31d3cff6f3b0ff6cbcf1c22850e8072b2108cbe3f8315561194ee66938
+size 585357

results.json

@@ -0,0 +1 @@
+{"mean_reward": 3481.5886947, "std_reward": 108.68079641634979, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-28T18:11:41.411789"}

train_eval_metrics.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3b6130af53c410e8a831b77cfe30541169bac5e1eeb86d9a5ff3d3e08388ce5a
+size 117501

trpo-Ant-v3.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d1df85582c394d8ccf01f0174463b6fc07a403ea6843891b1eae59dfc59503ba
+size 220478

trpo-Ant-v3/_stable_baselines3_version

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

trpo-Ant-v3/data

@@ -0,0 +1,103 @@
+{
+    "policy_class": {
+        ":type:": "<class 'abc.ABCMeta'>",
+        ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
+        "__module__": "stable_baselines3.common.policies",
+        "__doc__": "\n    Policy class for actor-critic algorithms (has both policy and value prediction).\n    Used by A2C, PPO and the likes.\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 ortho_init: Whether to use or not orthogonal initialization\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 full_std: Whether to use (n_features x n_actions) parameters\n        for the std instead of only (n_features,) when using gSDE\n    :param use_expln: Use ``expln()`` function instead of ``exp()`` 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 squash_output: Whether to squash the output using a tanh function,\n        this allows to ensure boundaries when using gSDE.\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 share_features_extractor: If True, the features extractor is shared between the policy and value networks.\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    ",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7fcc776d2ee0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fcc776d2f70>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fcc776d4040>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fcc776d40d0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fcc776d4160>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fcc776d41f0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fcc776d4280>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fcc776d4310>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fcc776d43a0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fcc776d4430>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fcc776d44c0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fcc776d4550>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7fcc776d1cc0>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "observation_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float64",
+        "_shape": [
+            111
+        ],
+        "low": "[-inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf]",
+        "high": "[inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n inf inf inf]",
+        "bounded_below": "[False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False]",
+        "bounded_above": "[False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False]",
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "_shape": [
+            8
+        ],
+        "low": "[-1. -1. -1. -1. -1. -1. -1. -1.]",
+        "high": "[1. 1. 1. 1. 1. 1. 1. 1.]",
+        "bounded_below": "[ True  True  True  True  True  True  True  True]",
+        "bounded_above": "[ True  True  True  True  True  True  True  True]",
+        "_np_random": "RandomState(MT19937)"
+    },
+    "n_envs": 1,
+    "num_timesteps": 1001472,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": 0,
+    "action_noise": null,
+    "start_time": 1676304990716977815,
+    "learning_rate": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "tensorboard_log": "runs/Ant-v3__trpo__2229209209__1676304987/Ant-v3",
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": null,
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYCAAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksChZSMAUOUdJRSlC4="
+    },
+    "_last_original_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "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"
+    },
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": -0.0014719999999999178,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 489,
+    "n_steps": 1024,
+    "gamma": 0.99,
+    "gae_lambda": 0.95,
+    "ent_coef": 0.0,
+    "vf_coef": 0.0,
+    "max_grad_norm": 0.0,
+    "normalize_advantage": true,
+    "batch_size": 128,
+    "cg_max_steps": 25,
+    "cg_damping": 0.1,
+    "line_search_shrinking_factor": 0.8,
+    "line_search_max_iter": 10,
+    "target_kl": 0.01,
+    "n_critic_updates": 20,
+    "sub_sampling_factor": 1
+}

trpo-Ant-v3/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7494c854086bfea565edb3b0ec7c1a970dcd66612c0659c5c447d31498d6b33f
+size 96175

trpo-Ant-v3/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3aac0040d90fa31801fdf64ffaad945cf45741bba9701d43f0e1753c32400d22
+size 97406

trpo-Ant-v3/pytorch_variables.pth

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

trpo-Ant-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

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a0c8be5a9d12d431843c9335b824c3de0001d23c850d309016448646b7976bcd
+size 9604