Upload PPO LunarLander-v2 trained agent
Browse files- .gitattributes +1 -0
- README.md +36 -0
- config.json +1 -0
- ppo-LunarLander-v2.zip +3 -0
- ppo-LunarLander-v2/_stable_baselines3_version +1 -0
- ppo-LunarLander-v2/data +94 -0
- ppo-LunarLander-v2/policy.optimizer.pth +3 -0
- ppo-LunarLander-v2/policy.pth +3 -0
- ppo-LunarLander-v2/pytorch_variables.pth +3 -0
- ppo-LunarLander-v2/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 +1,36 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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: 276.91 +/- 22.39
|
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 0x7f4738886c20>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f4738886cb0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f4738886d40>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f4738886dd0>", "_build": "<function ActorCriticPolicy._build at 0x7f4738886e60>", "forward": "<function ActorCriticPolicy.forward at 0x7f4738886ef0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f4738886f80>", "_predict": "<function ActorCriticPolicy._predict at 0x7f473888d050>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f473888d0e0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f473888d170>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f473888d200>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f47388c6e40>"}, "verbose": 1, "policy_kwargs": {}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "gASVwwEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLCIWUjANsb3eUjBVudW1weS5jb3JlLm11bHRpYXJyYXmUjAxfcmVjb25zdHJ1Y3SUk5RoBowHbmRhcnJheZSTlEsAhZRDAWKUh5RSlChLAUsIhZRoColDIAAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/lHSUYowEaGlnaJRoEmgUSwCFlGgWh5RSlChLAUsIhZRoColDIAAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/lHSUYowNYm91bmRlZF9iZWxvd5RoEmgUSwCFlGgWh5RSlChLAUsIhZRoB4wCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYolDCAAAAAAAAAAAlHSUYowNYm91bmRlZF9hYm92ZZRoEmgUSwCFlGgWh5RSlChLAUsIhZRoKolDCAAAAAAAAAAAlHSUYowKX25wX3JhbmRvbZROdWIu", "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:": "gASVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": 4, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1656388553.1066031, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "gASVvwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwNX2J1aWx0aW5fdHlwZZSTlIwKTGFtYmRhVHlwZZSFlFKUKGgCjAhDb2RlVHlwZZSFlFKUKEsBSwBLAUsBSxNDBIgAUwCUToWUKYwBX5SFlIxIL3Vzci9sb2NhbC9saWIvcHl0aG9uMy43L2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lIwEZnVuY5RLgEMCAAGUjAN2YWyUhZQpdJRSlH2UKIwLX19wYWNrYWdlX1+UjBhzdGFibGVfYmFzZWxpbmVzMy5jb21tb26UjAhfX25hbWVfX5SMHnN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi51dGlsc5SMCF9fZmlsZV9flIxIL3Vzci9sb2NhbC9saWIvcHl0aG9uMy43L2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaCB9lH2UKGgXaA6MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgYjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz8zqSowVTJhhZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gASVjQIAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSxBLCIaUaAOMBWR0eXBllJOUjAJmNJSJiIeUUpQoSwOMATyUTk5OSv////9K/////0sAdJRiiUIAAgAA2OeNvpuLKD8MSpk9EqHjvrp4O74rAgM+AAAAAAAAAABmW6o8IFa6PzSAsz55+Ic+3arBvGfHoL0AAAAAAAAAAA14DT7xHYI/ynbHPAxvtL5tolA+FvhUvQAAAAAAAAAAAGCLOo9Odrpic4U4I73lM7kWWDu0v5m3AACAPwAAgD/mYeO96s93P4asIb65XeO+dzj/vXPIRjwAAAAAAAAAAIBHnb17OO+6ROMhPr9EVb7wubA8oA56vQAAAAAAAIA/zei4u8OxI7oPVcaycpV6MH8e3zr6NIczAACAPwAAgD9aGos9G5KhP7SYuT7yss2+K4wBPnvBhj4AAAAAAAAAAGY+Uz1GfjQ/KkHdvTSsyb47gys9diH7vQAAAAAAAAAAZh9DvgzCiT+iIgi/S4cXv9NJd77MPgK+AAAAAAAAAAAaZmO9saoePjL5qr16Y5a+6rAmveNJ2b0AAAAAAAAAAKpgiT5mC6g/XQzsPpeOor5J/eU+xbM5PgAAAAAAAAAAAN7+vFTtjT5Sf/S76k+Ovk5BhjxiAXa9AAAAAAAAAAC2vY2+JU+6Phcagz6Uv6G+xjkSvUqjRD4AAAAAAAAAAK0IGb6T+uM+tTwBPhzRob410OM7AqwGvAAAAAAAAAAAmks5PIgw8T7bys69D6qCvp3yhTscfRa9AAAAAAAAAACUdJRiLg=="}, "_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": -0.015808000000000044, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 372, "n_steps": 1024, "gamma": 0.999, "gae_lambda": 0.98, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 4, "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": "0.21.0"}}
|
ppo-LunarLander-v2.zip
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:26bd743e20e5703fe9e2919dec15dc4ade069345d3126033c9a1e49c5a12d71e
|
3 |
+
size 144182
|
ppo-LunarLander-v2/_stable_baselines3_version
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
1.5.0
|
ppo-LunarLander-v2/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 0x7f4738886c20>",
|
8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f4738886cb0>",
|
9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f4738886d40>",
|
10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f4738886dd0>",
|
11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7f4738886e60>",
|
12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7f4738886ef0>",
|
13 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f4738886f80>",
|
14 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7f473888d050>",
|
15 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f473888d0e0>",
|
16 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f473888d170>",
|
17 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7f473888d200>",
|
18 |
+
"__abstractmethods__": "frozenset()",
|
19 |
+
"_abc_impl": "<_abc_data object at 0x7f47388c6e40>"
|
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 |
+
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:": "gASVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
|
39 |
+
"n": 4,
|
40 |
+
"_shape": [],
|
41 |
+
"dtype": "int64",
|
42 |
+
"_np_random": null
|
43 |
+
},
|
44 |
+
"n_envs": 16,
|
45 |
+
"num_timesteps": 1015808,
|
46 |
+
"_total_timesteps": 1000000,
|
47 |
+
"_num_timesteps_at_start": 0,
|
48 |
+
"seed": null,
|
49 |
+
"action_noise": null,
|
50 |
+
"start_time": 1656388553.1066031,
|
51 |
+
"learning_rate": 0.0003,
|
52 |
+
"tensorboard_log": null,
|
53 |
+
"lr_schedule": {
|
54 |
+
":type:": "<class 'function'>",
|
55 |
+
":serialized:": "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"
|
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": -0.015808000000000044,
|
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": 372,
|
79 |
+
"n_steps": 1024,
|
80 |
+
"gamma": 0.999,
|
81 |
+
"gae_lambda": 0.98,
|
82 |
+
"ent_coef": 0.01,
|
83 |
+
"vf_coef": 0.5,
|
84 |
+
"max_grad_norm": 0.5,
|
85 |
+
"batch_size": 64,
|
86 |
+
"n_epochs": 4,
|
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 |
+
}
|
ppo-LunarLander-v2/policy.optimizer.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:316bba28fa61972d3fa3019a7f12dd1a8247db334b6f7ee83a1dcddc40ed0dcd
|
3 |
+
size 84893
|
ppo-LunarLander-v2/policy.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:befbd828bea565a9daf032c76fd73d829bc88c2572a7508319834892d2236d97
|
3 |
+
size 43201
|
ppo-LunarLander-v2/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-LunarLander-v2/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: 0.21.0
|
replay.mp4
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:6ea73c005aaf013c697c138042c3a997c0fe5c69d5e653a2ba93771d92d2f875
|
3 |
+
size 199783
|
results.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"mean_reward": 276.90635999165806, "std_reward": 22.386333663159416, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-06-28T04:21:30.018167"}
|