Upload PPO LunarLander-v1 trained agent
Browse files- .gitattributes +1 -0
- README.md +28 -0
- config.json +1 -0
- ppo-LunarLander-v2.zip +3 -0
- ppo-LunarLander-v2/_stable_baselines3_version +1 -0
- ppo-LunarLander-v2/data +91 -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 +1,28 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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: 295.94 +/- 13.13
|
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 0x12e210280>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x12e210310>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x12e2103a0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x12e210430>", "_build": "<function ActorCriticPolicy._build at 0x12e2104c0>", "forward": "<function ActorCriticPolicy.forward at 0x12e210550>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x12e2105e0>", "_predict": "<function ActorCriticPolicy._predict at 0x12e210670>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x12e210700>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x12e210790>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x12e210820>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x12e20fcc0>"}, "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": 6946816, "_total_timesteps": 6915744, "_num_timesteps_at_start": 6815744, "seed": null, "action_noise": null, "start_time": 1651695518.7732532, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": null, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAQAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.004492936696326444, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 2120, "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": "macOS-12.3.1-arm64-arm-64bit Darwin Kernel Version 21.4.0: Fri Mar 18 00:46:32 PDT 2022; root:xnu-8020.101.4~15/RELEASE_ARM64_T6000", "Python": "3.9.10", "Stable-Baselines3": "1.5.0", "PyTorch": "1.11.0", "GPU Enabled": "False", "Numpy": "1.22.3", "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:af623286b8bf47590518bbfb22208b1381d409f3e3497a7a956d54974de58bcf
|
3 |
+
size 142974
|
ppo-LunarLander-v2/_stable_baselines3_version
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
1.5.0
|
ppo-LunarLander-v2/data
ADDED
@@ -0,0 +1,91 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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 0x12e210280>",
|
8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x12e210310>",
|
9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x12e2103a0>",
|
10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x12e210430>",
|
11 |
+
"_build": "<function ActorCriticPolicy._build at 0x12e2104c0>",
|
12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x12e210550>",
|
13 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x12e2105e0>",
|
14 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x12e210670>",
|
15 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x12e210700>",
|
16 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x12e210790>",
|
17 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x12e210820>",
|
18 |
+
"__abstractmethods__": "frozenset()",
|
19 |
+
"_abc_impl": "<_abc._abc_data object at 0x12e20fcc0>"
|
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": 6946816,
|
46 |
+
"_total_timesteps": 6915744,
|
47 |
+
"_num_timesteps_at_start": 6815744,
|
48 |
+
"seed": null,
|
49 |
+
"action_noise": null,
|
50 |
+
"start_time": 1651695518.7732532,
|
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": null,
|
58 |
+
"_last_episode_starts": {
|
59 |
+
":type:": "<class 'numpy.ndarray'>",
|
60 |
+
":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAQAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="
|
61 |
+
},
|
62 |
+
"_last_original_obs": null,
|
63 |
+
"_episode_num": 0,
|
64 |
+
"use_sde": false,
|
65 |
+
"sde_sample_freq": -1,
|
66 |
+
"_current_progress_remaining": -0.004492936696326444,
|
67 |
+
"ep_info_buffer": {
|
68 |
+
":type:": "<class 'collections.deque'>",
|
69 |
+
":serialized:": "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"
|
70 |
+
},
|
71 |
+
"ep_success_buffer": {
|
72 |
+
":type:": "<class 'collections.deque'>",
|
73 |
+
":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
|
74 |
+
},
|
75 |
+
"_n_updates": 2120,
|
76 |
+
"n_steps": 2048,
|
77 |
+
"gamma": 0.99,
|
78 |
+
"gae_lambda": 0.95,
|
79 |
+
"ent_coef": 0.0,
|
80 |
+
"vf_coef": 0.5,
|
81 |
+
"max_grad_norm": 0.5,
|
82 |
+
"batch_size": 64,
|
83 |
+
"n_epochs": 10,
|
84 |
+
"clip_range": {
|
85 |
+
":type:": "<class 'function'>",
|
86 |
+
":serialized:": "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"
|
87 |
+
},
|
88 |
+
"clip_range_vf": null,
|
89 |
+
"normalize_advantage": true,
|
90 |
+
"target_kl": null
|
91 |
+
}
|
ppo-LunarLander-v2/policy.optimizer.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:f8b0d41875082e158d36085ae577d3848626468a4810fe3c18ed981e1c258925
|
3 |
+
size 84637
|
ppo-LunarLander-v2/policy.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:31bf08b7bf4e9caceb14d86701e4473cd96f8a846ed5150bff89f390acbf2215
|
3 |
+
size 43073
|
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: macOS-12.3.1-arm64-arm-64bit Darwin Kernel Version 21.4.0: Fri Mar 18 00:46:32 PDT 2022; root:xnu-8020.101.4~15/RELEASE_ARM64_T6000
|
2 |
+
Python: 3.9.10
|
3 |
+
Stable-Baselines3: 1.5.0
|
4 |
+
PyTorch: 1.11.0
|
5 |
+
GPU Enabled: False
|
6 |
+
Numpy: 1.22.3
|
7 |
+
Gym: 0.21.0
|
replay.mp4
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:16b78a8689ebc44e608bba48a864e9c815bd721bb947c44092a9b74a0aa7d185
|
3 |
+
size 369184
|
results.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"mean_reward": 295.9390457193761, "std_reward": 13.129464372715917, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-04T23:22:59.693043"}
|