erniechiew
commited on
Commit
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0c09359
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Parent(s):
2bba474
PPO default with more iterations
Browse files- README.md +1 -1
- config.json +1 -1
- ppo_100m.zip +3 -0
- ppo_100m/_stable_baselines3_version +1 -0
- ppo_100m/data +94 -0
- ppo_100m/policy.optimizer.pth +3 -0
- ppo_100m/policy.pth +3 -0
- ppo_100m/pytorch_variables.pth +3 -0
- ppo_100m/system_info.txt +7 -0
- replay.mp4 +0 -0
- results.json +1 -1
README.md
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type: LunarLander-v2
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metrics:
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- type: mean_reward
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value:
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name: mean_reward
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verified: false
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---
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type: LunarLander-v2
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metrics:
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- type: mean_reward
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value: 291.06 +/- 23.12
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name: mean_reward
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verified: false
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---
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config.json
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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()`` 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 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 0x7f9a88164430>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f9a881644c0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f9a88164550>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f9a881645e0>", "_build": "<function 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"__module__": "stable_baselines3.common.policies",
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"__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 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()`` 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 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 ",
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},
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ppo_100m/policy.optimizer.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:0f53516fef90a36f503190f84bd54b8b02d025150726c79a3836941fc6e67aa1
|
3 |
+
size 87929
|
ppo_100m/policy.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:bce93a3382d3902e3cd5b7434ee8307d9c108b9e85e6848d82f1ed5da103df6c
|
3 |
+
size 43201
|
ppo_100m/pytorch_variables.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
|
3 |
+
size 431
|
ppo_100m/system_info.txt
ADDED
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
OS: Linux-5.15.0-56-generic-x86_64-with-glibc2.17 #62~20.04.1-Ubuntu SMP Tue Nov 22 21:24:20 UTC 2022
|
2 |
+
Python: 3.8.15
|
3 |
+
Stable-Baselines3: 1.6.2
|
4 |
+
PyTorch: 1.13.0+cu117
|
5 |
+
GPU Enabled: True
|
6 |
+
Numpy: 1.23.5
|
7 |
+
Gym: 0.21.0
|
replay.mp4
CHANGED
Binary files a/replay.mp4 and b/replay.mp4 differ
|
|
results.json
CHANGED
@@ -1 +1 @@
|
|
1 |
-
{"mean_reward":
|
|
|
1 |
+
{"mean_reward": 291.0646233476815, "std_reward": 23.119697417523252, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-18T18:45:15.615666"}
|