Upload PPO LunarLander-v2 trained agent
Browse files- .gitattributes +1 -0
- 1pt5m_attempt.zip +3 -0
- 1pt5m_attempt/_stable_baselines3_version +1 -0
- 1pt5m_attempt/data +94 -0
- 1pt5m_attempt/policy.optimizer.pth +3 -0
- 1pt5m_attempt/policy.pth +3 -0
- 1pt5m_attempt/pytorch_variables.pth +3 -0
- 1pt5m_attempt/system_info.txt +7 -0
- README.md +36 -0
- config.json +1 -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
|
1pt5m_attempt.zip
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:3582af2c561c242c284ea2f52f83264acd7f31cca903f500861af0c77b5922b2
|
3 |
+
size 144091
|
1pt5m_attempt/_stable_baselines3_version
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
1.5.0
|
1pt5m_attempt/data
ADDED
@@ -0,0 +1,94 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"policy_class": {
|
3 |
+
":type:": "<class 'abc.ABCMeta'>",
|
4 |
+
":serialized:": "gASVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
|
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 0x7f752f5b85f0>",
|
8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f752f5b8680>",
|
9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f752f5b8710>",
|
10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f752f5b87a0>",
|
11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7f752f5b8830>",
|
12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7f752f5b88c0>",
|
13 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f752f5b8950>",
|
14 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7f752f5b89e0>",
|
15 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f752f5b8a70>",
|
16 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f752f5b8b00>",
|
17 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7f752f5b8b90>",
|
18 |
+
"__abstractmethods__": "frozenset()",
|
19 |
+
"_abc_impl": "<_abc_data object at 0x7f752f58f180>"
|
20 |
+
},
|
21 |
+
"verbose": 1,
|
22 |
+
"policy_kwargs": {},
|
23 |
+
"observation_space": {
|
24 |
+
":type:": "<class 'gym.spaces.box.Box'>",
|
25 |
+
":serialized:": "gASVwgEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBXNoYXBllEsIhZSMA2xvd5SMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlGgGjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSwiFlGgKiUMgAACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP+UdJRijARoaWdolGgSaBRLAIWUaBaHlFKUKEsBSwiFlGgKiUMgAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH+UdJRijA1ib3VuZGVkX2JlbG93lGgSaBRLAIWUaBaHlFKUKEsBSwiFlGgHjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiiUMIAAAAAAAAAACUdJRijA1ib3VuZGVkX2Fib3ZllGgSaBRLAIWUaBaHlFKUKEsBSwiFlGgqiUMIAAAAAAAAAACUdJRijApfbnBfcmFuZG9tlE51Yi4=",
|
26 |
+
"dtype": "float32",
|
27 |
+
"shape": [
|
28 |
+
8
|
29 |
+
],
|
30 |
+
"low": "[-inf -inf -inf -inf -inf -inf -inf -inf]",
|
31 |
+
"high": "[inf inf inf inf inf inf inf inf]",
|
32 |
+
"bounded_below": "[False False False False False False False False]",
|
33 |
+
"bounded_above": "[False False False False False False False False]",
|
34 |
+
"_np_random": null
|
35 |
+
},
|
36 |
+
"action_space": {
|
37 |
+
":type:": "<class 'gym.spaces.discrete.Discrete'>",
|
38 |
+
":serialized:": "gASVgQAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwFc2hhcGWUKYwFZHR5cGWUjAVudW1weZRoB5OUjAJpOJSJiIeUUpQoSwOMATyUTk5OSv////9K/////0sAdJRijApfbnBfcmFuZG9tlE51Yi4=",
|
39 |
+
"n": 4,
|
40 |
+
"shape": [],
|
41 |
+
"dtype": "int64",
|
42 |
+
"_np_random": null
|
43 |
+
},
|
44 |
+
"n_envs": 16,
|
45 |
+
"num_timesteps": 1507328,
|
46 |
+
"_total_timesteps": 1500000,
|
47 |
+
"_num_timesteps_at_start": 0,
|
48 |
+
"seed": null,
|
49 |
+
"action_noise": null,
|
50 |
+
"start_time": 1657094386.6359262,
|
51 |
+
"learning_rate": 0.0003,
|
52 |
+
"tensorboard_log": null,
|
53 |
+
"lr_schedule": {
|
54 |
+
":type:": "<class 'function'>",
|
55 |
+
":serialized:": "gASVvwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwNX2J1aWx0aW5fdHlwZZSTlIwKTGFtYmRhVHlwZZSFlFKUKGgCjAhDb2RlVHlwZZSFlFKUKEsBSwBLAUsBSxNDBIgAUwCUToWUKYwBX5SFlIxIL3Vzci9sb2NhbC9saWIvcHl0aG9uMy43L2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lIwEZnVuY5RLgEMCAAGUjAN2YWyUhZQpdJRSlH2UKIwLX19wYWNrYWdlX1+UjBhzdGFibGVfYmFzZWxpbmVzMy5jb21tb26UjAhfX25hbWVfX5SMHnN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi51dGlsc5SMCF9fZmlsZV9flIxIL3Vzci9sb2NhbC9saWIvcHl0aG9uMy43L2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaCB9lH2UKGgXaA6MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgYjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz8zqSowVTJhhZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"
|
56 |
+
},
|
57 |
+
"_last_obs": {
|
58 |
+
":type:": "<class 'numpy.ndarray'>",
|
59 |
+
":serialized:": "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"
|
60 |
+
},
|
61 |
+
"_last_episode_starts": {
|
62 |
+
":type:": "<class 'numpy.ndarray'>",
|
63 |
+
":serialized:": "gASVmAAAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSxCFlGgDjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYolDEAAAAAAAAAAAAAAAAAAAAACUdJRiLg=="
|
64 |
+
},
|
65 |
+
"_last_original_obs": null,
|
66 |
+
"_episode_num": 0,
|
67 |
+
"use_sde": false,
|
68 |
+
"sde_sample_freq": -1,
|
69 |
+
"_current_progress_remaining": -0.004885333333333408,
|
70 |
+
"ep_info_buffer": {
|
71 |
+
":type:": "<class 'collections.deque'>",
|
72 |
+
":serialized:": "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"
|
73 |
+
},
|
74 |
+
"ep_success_buffer": {
|
75 |
+
":type:": "<class 'collections.deque'>",
|
76 |
+
":serialized:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
|
77 |
+
},
|
78 |
+
"_n_updates": 460,
|
79 |
+
"n_steps": 2048,
|
80 |
+
"gamma": 0.99,
|
81 |
+
"gae_lambda": 0.95,
|
82 |
+
"ent_coef": 0.0,
|
83 |
+
"vf_coef": 0.5,
|
84 |
+
"max_grad_norm": 0.5,
|
85 |
+
"batch_size": 64,
|
86 |
+
"n_epochs": 10,
|
87 |
+
"clip_range": {
|
88 |
+
":type:": "<class 'function'>",
|
89 |
+
":serialized:": "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"
|
90 |
+
},
|
91 |
+
"clip_range_vf": null,
|
92 |
+
"normalize_advantage": true,
|
93 |
+
"target_kl": null
|
94 |
+
}
|
1pt5m_attempt/policy.optimizer.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:a6cd163a4ab1630c52c90f707664bfe04f939b8ae5d65e37c4bc857e62eb3c80
|
3 |
+
size 84893
|
1pt5m_attempt/policy.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:3e8bdb901d5210e89081cefb328a2469736532ac426945c0f0d08009d6473bac
|
3 |
+
size 43201
|
1pt5m_attempt/pytorch_variables.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
|
3 |
+
size 431
|
1pt5m_attempt/system_info.txt
ADDED
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
OS: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022
|
2 |
+
Python: 3.7.13
|
3 |
+
Stable-Baselines3: 1.5.0
|
4 |
+
PyTorch: 1.11.0+cu113
|
5 |
+
GPU Enabled: True
|
6 |
+
Numpy: 1.21.6
|
7 |
+
Gym: 0.17.3
|
README.md
ADDED
@@ -0,0 +1,36 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
---
|
2 |
+
library_name: stable-baselines3
|
3 |
+
tags:
|
4 |
+
- LunarLander-v2
|
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: 275.55 +/- 24.60
|
14 |
+
name: mean_reward
|
15 |
+
task:
|
16 |
+
type: reinforcement-learning
|
17 |
+
name: reinforcement-learning
|
18 |
+
dataset:
|
19 |
+
name: LunarLander-v2
|
20 |
+
type: LunarLander-v2
|
21 |
+
---
|
22 |
+
|
23 |
+
# **PPO** Agent playing **LunarLander-v2**
|
24 |
+
This is a trained model of a **PPO** agent playing **LunarLander-v2**
|
25 |
+
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
|
26 |
+
|
27 |
+
## Usage (with Stable-baselines3)
|
28 |
+
TODO: Add your code
|
29 |
+
|
30 |
+
|
31 |
+
```python
|
32 |
+
from stable_baselines3 import ...
|
33 |
+
from huggingface_sb3 import load_from_hub
|
34 |
+
|
35 |
+
...
|
36 |
+
```
|
config.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gASVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__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 0x7f752f5b85f0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f752f5b8680>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f752f5b8710>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f752f5b87a0>", "_build": "<function ActorCriticPolicy._build at 0x7f752f5b8830>", "forward": "<function ActorCriticPolicy.forward at 0x7f752f5b88c0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f752f5b8950>", "_predict": "<function ActorCriticPolicy._predict at 0x7f752f5b89e0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f752f5b8a70>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f752f5b8b00>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f752f5b8b90>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f752f58f180>"}, "verbose": 1, "policy_kwargs": {}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "shape": [8], "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]", "high": "[inf inf inf inf inf inf inf inf]", "bounded_below": "[False False False False False False False False]", "bounded_above": "[False False False False False False False False]", "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.discrete.Discrete'>", ":serialized:": "gASVgQAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwFc2hhcGWUKYwFZHR5cGWUjAVudW1weZRoB5OUjAJpOJSJiIeUUpQoSwOMATyUTk5OSv////9K/////0sAdJRijApfbnBfcmFuZG9tlE51Yi4=", "n": 4, "shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "num_timesteps": 1507328, "_total_timesteps": 1500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1657094386.6359262, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gASVjQIAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSxBLCIaUaAOMBWR0eXBllJOUjAJmNJSJiIeUUpQoSwOMATyUTk5OSv////9K/////0sAdJRiiUIAAgAAM+y5vSkYKD/P4IG9uponv5l3DL6lSaY8AAAAAAAAAAAzyYe8R5yzP4a3zL67dQC+ElPvO/7S87wAAAAAAAAAAGaWFj7oo4g+WiAVv0TbU766nyi+pUH+vgAAAAAAAAAAM91zPLTt5D5NW4c9MYL0vjbGTTzyXl+8AAAAAAAAAAAAwbE99vRuuo4WNDZfqgE27RKZuloFDrcAAAAAAACAP81+H72PBlW6ZdlgszLM7S7GlYs69EO5MwAAgD8AAIA/GoT+PT3YZrtGCim77mKjO9/eEr1zcY48AACAPwAAgD9Gy1e+JFZGPksebD5NSp2+6QS8u/k9ljwAAAAAAAAAAM0YvD0U6uE+ZMuqPZ5f7b4TGOc91zCWvQAAAAAAAAAAAEu3PD3fU7sdEOO9yu0EPR/ShzwaH+C9AACAPwAAgD/mrWM+PaeYPr7fkb6NsNC+oDABvUeuTDsAAAAAAAAAALN5Gj1gH6c+QqXCPRCMnb7vvz89c2uzPQAAAAAAAAAAzcPMvHu+hLo+09i5L482tH3ZQLtdsPo4AACAPwAAgD+tOwa+uOzau0qqFTt00dI4jk4uPYYYSroAAIA/AACAP4Cccr1avAk+XjEJPnm6r74Dx9w8I6ODPAAAAAAAAAAA4PFMPrfifz965xc+vWQfv0b4bz67GSy9AAAAAAAAAACUdJRiLg=="}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gASVmAAAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSxCFlGgDjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYolDEAAAAAAAAAAAAAAAAAAAAACUdJRiLg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.004885333333333408, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 460, "n_steps": 2048, "gamma": 0.99, "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-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022", "Python": "3.7.13", "Stable-Baselines3": "1.5.0", "PyTorch": "1.11.0+cu113", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.17.3"}}
|
replay.mp4
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:875716ab8c5233626ee75e1e60e78219889a5cabbce420fbb984d2ef0854033d
|
3 |
+
size 202592
|
results.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"mean_reward": 275.5456453435734, "std_reward": 24.60094882944731, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-07-06T08:33:29.388273"}
|