First commit
Browse files- .gitattributes +1 -0
- README.md +28 -0
- config.json +1 -0
- ppo-Pendulum-v1.zip +3 -0
- ppo-Pendulum-v1/_stable_baselines3_version +1 -0
- ppo-Pendulum-v1/data +99 -0
- ppo-Pendulum-v1/policy.optimizer.pth +3 -0
- ppo-Pendulum-v1/policy.pth +3 -0
- ppo-Pendulum-v1/pytorch_variables.pth +3 -0
- ppo-Pendulum-v1/system_info.txt +7 -0
- replay.mp4 +3 -0
- results.json +1 -0
.gitattributes
CHANGED
@@ -25,3 +25,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
|
25 |
*.zip filter=lfs diff=lfs merge=lfs -text
|
26 |
*.zstandard filter=lfs diff=lfs merge=lfs -text
|
27 |
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
|
|
|
25 |
*.zip filter=lfs diff=lfs merge=lfs -text
|
26 |
*.zstandard filter=lfs diff=lfs merge=lfs -text
|
27 |
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
28 |
+
*.mp4 filter=lfs diff=lfs merge=lfs -text
|
README.md
ADDED
@@ -0,0 +1,28 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
---
|
2 |
+
library_name: stable-baselines3
|
3 |
+
tags:
|
4 |
+
- Pendulum-v1
|
5 |
+
- deep-reinforcement-learning
|
6 |
+
- reinforcement-learning
|
7 |
+
- stable-baselines3
|
8 |
+
model-index:
|
9 |
+
- name: PPO
|
10 |
+
results:
|
11 |
+
- metrics:
|
12 |
+
- type: mean_reward
|
13 |
+
value: -272.21 +/- 159.73
|
14 |
+
name: mean_reward
|
15 |
+
task:
|
16 |
+
type: reinforcement-learning
|
17 |
+
name: reinforcement-learning
|
18 |
+
dataset:
|
19 |
+
name: Pendulum-v1
|
20 |
+
type: Pendulum-v1
|
21 |
+
---
|
22 |
+
|
23 |
+
# **PPO** Agent playing **Pendulum-v1**
|
24 |
+
This is a trained model of a **PPO** agent playing **Pendulum-v1** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
|
25 |
+
|
26 |
+
## Usage (with Stable-baselines3)
|
27 |
+
TODO: Add your code
|
28 |
+
|
config.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"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 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 ", "__init__": "<function ActorCriticPolicy.__init__ at 0x7f74713728c0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f7471372950>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f74713729e0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f7471372a70>", "_build": "<function ActorCriticPolicy._build at 0x7f7471372b00>", "forward": "<function ActorCriticPolicy.forward at 0x7f7471372b90>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f7471372c20>", "_predict": "<function ActorCriticPolicy._predict at 0x7f7471372cb0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f7471372d40>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f7471372dd0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f7471372e60>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f7471349390>"}, "verbose": 1, "policy_kwargs": {}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [3], "low": "[-1. -1. -8.]", "high": "[1. 1. 8.]", "bounded_below": "[ True True True]", "bounded_above": "[ True True True]", "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "gAWVWQEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLAYWUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWBAAAAAAAAAAAAADAlGgKSwGFlIwBQ5R0lFKUjARoaWdolGgSKJYEAAAAAAAAAAAAAECUaApLAYWUaBV0lFKUjA1ib3VuZGVkX2JlbG93lGgSKJYBAAAAAAAAAAGUaAeMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLAYWUaBV0lFKUjA1ib3VuZGVkX2Fib3ZllGgSKJYBAAAAAAAAAAGUaCFLAYWUaBV0lFKUjApfbnBfcmFuZG9tlE51Yi4=", "dtype": "float32", "_shape": [1], "low": "[-2.]", "high": "[2.]", "bounded_below": "[ True]", "bounded_above": "[ True]", "_np_random": null}, "n_envs": 1, "num_timesteps": 100352, "_total_timesteps": 100000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1651656807.660387, "learning_rate": 0.001, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVgQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYMAAAAAAAAAHnwfz9RULK8EVDfvZSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGJLAUsDhpSMAUOUdJRSlC4="}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": true, "sde_sample_freq": 4, "_current_progress_remaining": -0.0035199999999999676, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 490, "n_steps": 2048, "gamma": 0.98, "gae_lambda": 0.95, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 10, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.4.0-109-generic-x86_64-with-debian-bullseye-sid #123-Ubuntu SMP Fri Apr 8 09:10:54 UTC 2022", "Python": "3.7.9", "Stable-Baselines3": "1.5.1a5", "PyTorch": "1.11.0+cpu", "GPU Enabled": "False", "Numpy": "1.20.2", "Gym": "0.21.0"}}
|
ppo-Pendulum-v1.zip
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:00aadf63e4759008a054ce7cc1f32d01d6b53f8dfcf70af0a19af5b917a2eb2b
|
3 |
+
size 134677
|
ppo-Pendulum-v1/_stable_baselines3_version
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
1.5.1a5
|
ppo-Pendulum-v1/data
ADDED
@@ -0,0 +1,99 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"policy_class": {
|
3 |
+
":type:": "<class 'abc.ABCMeta'>",
|
4 |
+
":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
|
5 |
+
"__module__": "stable_baselines3.common.policies",
|
6 |
+
"__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 ",
|
7 |
+
"__init__": "<function ActorCriticPolicy.__init__ at 0x7f74713728c0>",
|
8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f7471372950>",
|
9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f74713729e0>",
|
10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f7471372a70>",
|
11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7f7471372b00>",
|
12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7f7471372b90>",
|
13 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f7471372c20>",
|
14 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7f7471372cb0>",
|
15 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f7471372d40>",
|
16 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f7471372dd0>",
|
17 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7f7471372e60>",
|
18 |
+
"__abstractmethods__": "frozenset()",
|
19 |
+
"_abc_impl": "<_abc_data object at 0x7f7471349390>"
|
20 |
+
},
|
21 |
+
"verbose": 1,
|
22 |
+
"policy_kwargs": {},
|
23 |
+
"observation_space": {
|
24 |
+
":type:": "<class 'gym.spaces.box.Box'>",
|
25 |
+
":serialized:": "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",
|
26 |
+
"dtype": "float32",
|
27 |
+
"_shape": [
|
28 |
+
3
|
29 |
+
],
|
30 |
+
"low": "[-1. -1. -8.]",
|
31 |
+
"high": "[1. 1. 8.]",
|
32 |
+
"bounded_below": "[ True True True]",
|
33 |
+
"bounded_above": "[ True True True]",
|
34 |
+
"_np_random": null
|
35 |
+
},
|
36 |
+
"action_space": {
|
37 |
+
":type:": "<class 'gym.spaces.box.Box'>",
|
38 |
+
":serialized:": "gAWVWQEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLAYWUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWBAAAAAAAAAAAAADAlGgKSwGFlIwBQ5R0lFKUjARoaWdolGgSKJYEAAAAAAAAAAAAAECUaApLAYWUaBV0lFKUjA1ib3VuZGVkX2JlbG93lGgSKJYBAAAAAAAAAAGUaAeMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLAYWUaBV0lFKUjA1ib3VuZGVkX2Fib3ZllGgSKJYBAAAAAAAAAAGUaCFLAYWUaBV0lFKUjApfbnBfcmFuZG9tlE51Yi4=",
|
39 |
+
"dtype": "float32",
|
40 |
+
"_shape": [
|
41 |
+
1
|
42 |
+
],
|
43 |
+
"low": "[-2.]",
|
44 |
+
"high": "[2.]",
|
45 |
+
"bounded_below": "[ True]",
|
46 |
+
"bounded_above": "[ True]",
|
47 |
+
"_np_random": null
|
48 |
+
},
|
49 |
+
"n_envs": 1,
|
50 |
+
"num_timesteps": 100352,
|
51 |
+
"_total_timesteps": 100000,
|
52 |
+
"_num_timesteps_at_start": 0,
|
53 |
+
"seed": null,
|
54 |
+
"action_noise": null,
|
55 |
+
"start_time": 1651656807.660387,
|
56 |
+
"learning_rate": 0.001,
|
57 |
+
"tensorboard_log": null,
|
58 |
+
"lr_schedule": {
|
59 |
+
":type:": "<class 'function'>",
|
60 |
+
":serialized:": "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"
|
61 |
+
},
|
62 |
+
"_last_obs": {
|
63 |
+
":type:": "<class 'numpy.ndarray'>",
|
64 |
+
":serialized:": "gAWVgQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYMAAAAAAAAAHnwfz9RULK8EVDfvZSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGJLAUsDhpSMAUOUdJRSlC4="
|
65 |
+
},
|
66 |
+
"_last_episode_starts": {
|
67 |
+
":type:": "<class 'numpy.ndarray'>",
|
68 |
+
":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="
|
69 |
+
},
|
70 |
+
"_last_original_obs": null,
|
71 |
+
"_episode_num": 0,
|
72 |
+
"use_sde": true,
|
73 |
+
"sde_sample_freq": 4,
|
74 |
+
"_current_progress_remaining": -0.0035199999999999676,
|
75 |
+
"ep_info_buffer": {
|
76 |
+
":type:": "<class 'collections.deque'>",
|
77 |
+
":serialized:": "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"
|
78 |
+
},
|
79 |
+
"ep_success_buffer": {
|
80 |
+
":type:": "<class 'collections.deque'>",
|
81 |
+
":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
|
82 |
+
},
|
83 |
+
"_n_updates": 490,
|
84 |
+
"n_steps": 2048,
|
85 |
+
"gamma": 0.98,
|
86 |
+
"gae_lambda": 0.95,
|
87 |
+
"ent_coef": 0.0,
|
88 |
+
"vf_coef": 0.5,
|
89 |
+
"max_grad_norm": 0.5,
|
90 |
+
"batch_size": 64,
|
91 |
+
"n_epochs": 10,
|
92 |
+
"clip_range": {
|
93 |
+
":type:": "<class 'function'>",
|
94 |
+
":serialized:": "gAWV0QIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwNX2J1aWx0aW5fdHlwZZSTlIwKTGFtYmRhVHlwZZSFlFKUKGgCjAhDb2RlVHlwZZSFlFKUKEsBSwBLAUsBSxNDBIgAUwCUToWUKYwBX5SFlIxRL2hvbWUvYW50b25pbi9Eb2N1bWVudHMvZGxyL3JsL3RvcmNoeS1iYXNlbGluZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lIwEZnVuY5RLgEMCAAGUjAN2YWyUhZQpdJRSlH2UKIwLX19wYWNrYWdlX1+UjBhzdGFibGVfYmFzZWxpbmVzMy5jb21tb26UjAhfX25hbWVfX5SMHnN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi51dGlsc5SMCF9fZmlsZV9flIxRL2hvbWUvYW50b25pbi9Eb2N1bWVudHMvZGxyL3JsL3RvcmNoeS1iYXNlbGluZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaCB9lH2UKGgXaA6MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgYjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz/JmZmZmZmahZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"
|
95 |
+
},
|
96 |
+
"clip_range_vf": null,
|
97 |
+
"normalize_advantage": true,
|
98 |
+
"target_kl": null
|
99 |
+
}
|
ppo-Pendulum-v1/policy.optimizer.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:ab7bb241ea8bdf9ba2cb0b273908ebea05f4558db996f851afbc36423cd0e12b
|
3 |
+
size 78871
|
ppo-Pendulum-v1/policy.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:28f38865aa31f8900ad8e354f1fbce0442fd64c47fdc0ae631c082fc5359872f
|
3 |
+
size 40254
|
ppo-Pendulum-v1/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-Pendulum-v1/system_info.txt
ADDED
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
OS: Linux-5.4.0-109-generic-x86_64-with-debian-bullseye-sid #123-Ubuntu SMP Fri Apr 8 09:10:54 UTC 2022
|
2 |
+
Python: 3.7.9
|
3 |
+
Stable-Baselines3: 1.5.1a5
|
4 |
+
PyTorch: 1.11.0+cpu
|
5 |
+
GPU Enabled: False
|
6 |
+
Numpy: 1.20.2
|
7 |
+
Gym: 0.21.0
|
replay.mp4
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:76a757969f0b4f5af3cec9fae2326ba90dc3f60a8d064e3b808e4fc1e855eb1c
|
3 |
+
size 203450
|
results.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"mean_reward": -272.21298450000006, "std_reward": 159.73207117998575, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-04T11:35:55.019292"}
|