one MILLION $!
Browse files- .gitattributes +1 -0
- README.md +28 -0
- config.json +1 -0
- ll2-ppo-default.zip +3 -0
- ll2-ppo-default/_stable_baselines3_version +1 -0
- ll2-ppo-default/data +94 -0
- ll2-ppo-default/policy.optimizer.pth +3 -0
- ll2-ppo-default/policy.pth +3 -0
- ll2-ppo-default/pytorch_variables.pth +3 -0
- ll2-ppo-default/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 |
+
- 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: 261.60 +/- 27.38
|
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** 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 0x7f74c03b30e0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f74c03b3170>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f74c03b3200>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f74c03b3290>", "_build": "<function ActorCriticPolicy._build at 0x7f74c03b3320>", "forward": "<function ActorCriticPolicy.forward at 0x7f74c03b33b0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f74c03b3440>", "_predict": "<function ActorCriticPolicy._predict at 0x7f74c03b34d0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f74c03b3560>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f74c03b35f0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f74c03b3680>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f74c03ff690>"}, "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:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////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": 1652529878.8505113, "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:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="}, "_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:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 310, "n_steps": 2048, "gamma": 0.999, "gae_lambda": 0.98, "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:": "gAWVvwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwNX2J1aWx0aW5fdHlwZZSTlIwKTGFtYmRhVHlwZZSFlFKUKGgCjAhDb2RlVHlwZZSFlFKUKEsBSwBLAUsBSxNDBIgAUwCUToWUKYwBX5SFlIxIL3Vzci9sb2NhbC9saWIvcHl0aG9uMy43L2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lIwEZnVuY5RLgEMCAAGUjAN2YWyUhZQpdJRSlH2UKIwLX19wYWNrYWdlX1+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"}}
|
ll2-ppo-default.zip
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:a0a509bd57f6fe915d97f1728834a358f8bf21ef76589cce60a58a7c5a47f6ab
|
3 |
+
size 144114
|
ll2-ppo-default/_stable_baselines3_version
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
1.5.0
|
ll2-ppo-default/data
ADDED
@@ -0,0 +1,94 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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 0x7f74c03b30e0>",
|
8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f74c03b3170>",
|
9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f74c03b3200>",
|
10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f74c03b3290>",
|
11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7f74c03b3320>",
|
12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7f74c03b33b0>",
|
13 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f74c03b3440>",
|
14 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7f74c03b34d0>",
|
15 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f74c03b3560>",
|
16 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f74c03b35f0>",
|
17 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7f74c03b3680>",
|
18 |
+
"__abstractmethods__": "frozenset()",
|
19 |
+
"_abc_impl": "<_abc_data object at 0x7f74c03ff690>"
|
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:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////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": 1652529878.8505113,
|
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:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="
|
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:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
|
77 |
+
},
|
78 |
+
"_n_updates": 310,
|
79 |
+
"n_steps": 2048,
|
80 |
+
"gamma": 0.999,
|
81 |
+
"gae_lambda": 0.98,
|
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 |
+
}
|
ll2-ppo-default/policy.optimizer.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:d1a60bc314d7531d64db903602d18ee80072786c2be93b56be731363acd8d8db
|
3 |
+
size 84893
|
ll2-ppo-default/policy.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:e4ee26ea0c34566e667393a74173a76847be267ba26b2ba3558e368580c2002f
|
3 |
+
size 43201
|
ll2-ppo-default/pytorch_variables.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
|
3 |
+
size 431
|
ll2-ppo-default/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 sha256:52f9181fd568c64cf7cf043cbd1406792c4a51d168c02584db6f37334eaf754c
|
3 |
+
size 233372
|
results.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"mean_reward": 261.6005103514896, "std_reward": 27.380924906244562, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-14T12:30:26.890253"}
|