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: 230.42 +/- 83.51
|
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 0x7efb4f09cb00>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7efb4f09cb90>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7efb4f09cc20>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7efb4f09ccb0>", "_build": "<function ActorCriticPolicy._build at 0x7efb4f09cd40>", "forward": "<function ActorCriticPolicy.forward at 0x7efb4f09cdd0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7efb4f09ce60>", "_predict": "<function ActorCriticPolicy._predict at 0x7efb4f09cef0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7efb4f09cf80>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7efb4f0a0050>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7efb4f0a00e0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7efb4f0e8810>"}, "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": 1653251520.7681022, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_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": 496, "n_steps": 1024, "gamma": 0.99, "gae_lambda": 0.95, "ent_coef": 0.05, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 4, "clip_range": {":type:": "<class 'function'>", ":serialized:": "gASVvwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwNX2J1aWx0aW5fdHlwZZSTlIwKTGFtYmRhVHlwZZSFlFKUKGgCjAhDb2RlVHlwZZSFlFKUKEsBSwBLAUsBSxNDBIgAUwCUToWUKYwBX5SFlIxIL3Vzci9sb2NhbC9saWIvcHl0aG9uMy43L2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lIwEZnVuY5RLgEMCAAGUjAN2YWyUhZQpdJRSlH2UKIwLX19wYWNrYWdlX1+UjBhzdGFibGVfYmFzZWxpbmVzMy5jb21tb26UjAhfX25hbWVfX5SMHnN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi51dGlsc5SMCF9fZmlsZV9flIxIL3Vzci9sb2NhbC9saWIvcHl0aG9uMy43L2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaCB9lH2UKGgXaA6MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgYjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz/JmZmZmZmahZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"}, "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 sha256:83cde3b0c91aab08f1c691810097e8616fcd2197294e980fff7887726752a1a9
|
3 |
+
size 144205
|
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 0x7efb4f09cb00>",
|
8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7efb4f09cb90>",
|
9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7efb4f09cc20>",
|
10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7efb4f09ccb0>",
|
11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7efb4f09cd40>",
|
12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7efb4f09cdd0>",
|
13 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7efb4f09ce60>",
|
14 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7efb4f09cef0>",
|
15 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7efb4f09cf80>",
|
16 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7efb4f0a0050>",
|
17 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7efb4f0a00e0>",
|
18 |
+
"__abstractmethods__": "frozenset()",
|
19 |
+
"_abc_impl": "<_abc_data object at 0x7efb4f0e8810>"
|
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": 1653251520.7681022,
|
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": 496,
|
79 |
+
"n_steps": 1024,
|
80 |
+
"gamma": 0.99,
|
81 |
+
"gae_lambda": 0.95,
|
82 |
+
"ent_coef": 0.05,
|
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 sha256:042ff4df49cd2dd6a8dfd5f4643bc5a2fda14a06aebff45b04e7c3130b5e063f
|
3 |
+
size 84893
|
ppo-LunarLander-v2/policy.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:803f85e3277a17419b7255b6b3312b9ee4b03d7daa3ac684ed8966af5545d991
|
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 sha256:21094422c5fa4e854dc677aa13e051a21cebc10ab5a0dd12045838bd64ad165b
|
3 |
+
size 208139
|
results.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"mean_reward": 230.42093134462107, "std_reward": 83.50692321825073, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-22T20:51:46.899780"}
|