Upload PPO LunarLander-v2 trained agent
Browse files- README.md +37 -0
- config.json +1 -0
- ppo-LunarLander-v2.zip +3 -0
- ppo-LunarLander-v2/_stable_baselines3_version +1 -0
- ppo-LunarLander-v2/data +95 -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 +0 -0
- results.json +1 -0
README.md
ADDED
@@ -0,0 +1,37 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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 |
+
- task:
|
12 |
+
type: reinforcement-learning
|
13 |
+
name: reinforcement-learning
|
14 |
+
dataset:
|
15 |
+
name: LunarLander-v2
|
16 |
+
type: LunarLander-v2
|
17 |
+
metrics:
|
18 |
+
- type: mean_reward
|
19 |
+
value: -97.11 +/- 39.31
|
20 |
+
name: mean_reward
|
21 |
+
verified: false
|
22 |
+
---
|
23 |
+
|
24 |
+
# **PPO** Agent playing **LunarLander-v2**
|
25 |
+
This is a trained model of a **PPO** agent playing **LunarLander-v2**
|
26 |
+
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
|
27 |
+
|
28 |
+
## Usage (with Stable-baselines3)
|
29 |
+
TODO: Add your code
|
30 |
+
|
31 |
+
|
32 |
+
```python
|
33 |
+
from stable_baselines3 import ...
|
34 |
+
from huggingface_sb3 import load_from_hub
|
35 |
+
|
36 |
+
...
|
37 |
+
```
|
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 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 share_features_extractor: If True, the features extractor is shared between the policy and value networks.\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 0x7f38846fff70>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f3884703040>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f38847030d0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f3884703160>", "_build": "<function ActorCriticPolicy._build at 0x7f38847031f0>", "forward": "<function ActorCriticPolicy.forward at 0x7f3884703280>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f3884703310>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f38847033a0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f3884703430>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f38847034c0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f3884703550>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f38847035e0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f3884701a80>"}, "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": 229376, "_total_timesteps": 200000.0, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1678695281623410863, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAAJpzXT3XX2M6osGOu4S29LzC2U485erYvQAAAAAAAIA/riq6vjdonr1yxYm+yrsIveAXnz4rLgy+AAAAAAAAAAD6YlO/N2wKvnBvxDwQG6i8cgMOvfq+jr0AAAAAAACAP8VV3r5rl5A+AE/DvuMSEb+pNsW9k1ujvQAAAAAAAAAAzbpivKwZmz/jwG28VI+vvhY0Sj0Qt5q9AAAAAAAAAADT+MA+pYLxPnb4tj5EVNK+wzQNPoN6+zwAAAAAAAAAAJD2t74BVaC8fRtIPHc6Ir344bM7ER+kvQAAAAAAAAAA0nijvqkXSbyrDtK8Zz/pPBv1yT2eLs07AAAAAAAAAAAN1ZK9uNqxOnrBsro0zuK7EtZ4O9rXubwAAAAAAAAAAJqovr07v8A/j2PLvixQWb2njDi9r5jIuwAAAAAAAAAATbUFPdXAgD/gr5o9XrKOvq8PwLvx6AE8AAAAAAAAAABOwiG/n2xEvtLLXj3DlI09+KATvngrpTwAAAAAAAAAADtNCb9O2tW8V/+rvD0hxDt3eEc+Mzp1OwAAAAAAAIA/jZbtvewvxDrXB6M9SbxzPCtrYrx28qw8AACAPwAAgD8yqq6+iPCsP7aoGL/OtqS+5cSDvhA7Tr0AAAAAAAAAANsnpr5xhyK7Y3DPPDuBlLrYjg07cYOnOwAAgD8AAIA/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksQSwiGlIwBQ5R0lFKULg=="}, "_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": -0.1468799999999999, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 70, "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-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.9.16", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}
|
ppo-LunarLander-v2.zip
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:0322b8a908decb2e9d8bddd37e8a2cc84b4b60fc2d01b7bbfdf25af00c6b9ba3
|
3 |
+
size 147329
|
ppo-LunarLander-v2/_stable_baselines3_version
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
1.7.0
|
ppo-LunarLander-v2/data
ADDED
@@ -0,0 +1,95 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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 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 share_features_extractor: If True, the features extractor is shared between the policy and value networks.\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 0x7f38846fff70>",
|
8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f3884703040>",
|
9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f38847030d0>",
|
10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f3884703160>",
|
11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7f38847031f0>",
|
12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7f3884703280>",
|
13 |
+
"extract_features": "<function ActorCriticPolicy.extract_features at 0x7f3884703310>",
|
14 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f38847033a0>",
|
15 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7f3884703430>",
|
16 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f38847034c0>",
|
17 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f3884703550>",
|
18 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7f38847035e0>",
|
19 |
+
"__abstractmethods__": "frozenset()",
|
20 |
+
"_abc_impl": "<_abc._abc_data object at 0x7f3884701a80>"
|
21 |
+
},
|
22 |
+
"verbose": 1,
|
23 |
+
"policy_kwargs": {},
|
24 |
+
"observation_space": {
|
25 |
+
":type:": "<class 'gym.spaces.box.Box'>",
|
26 |
+
":serialized:": "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",
|
27 |
+
"dtype": "float32",
|
28 |
+
"_shape": [
|
29 |
+
8
|
30 |
+
],
|
31 |
+
"low": "[-inf -inf -inf -inf -inf -inf -inf -inf]",
|
32 |
+
"high": "[inf inf inf inf inf inf inf inf]",
|
33 |
+
"bounded_below": "[False False False False False False False False]",
|
34 |
+
"bounded_above": "[False False False False False False False False]",
|
35 |
+
"_np_random": null
|
36 |
+
},
|
37 |
+
"action_space": {
|
38 |
+
":type:": "<class 'gym.spaces.discrete.Discrete'>",
|
39 |
+
":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
|
40 |
+
"n": 4,
|
41 |
+
"_shape": [],
|
42 |
+
"dtype": "int64",
|
43 |
+
"_np_random": null
|
44 |
+
},
|
45 |
+
"n_envs": 16,
|
46 |
+
"num_timesteps": 229376,
|
47 |
+
"_total_timesteps": 200000.0,
|
48 |
+
"_num_timesteps_at_start": 0,
|
49 |
+
"seed": null,
|
50 |
+
"action_noise": null,
|
51 |
+
"start_time": 1678695281623410863,
|
52 |
+
"learning_rate": 0.0003,
|
53 |
+
"tensorboard_log": null,
|
54 |
+
"lr_schedule": {
|
55 |
+
":type:": "<class 'function'>",
|
56 |
+
":serialized:": "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"
|
57 |
+
},
|
58 |
+
"_last_obs": {
|
59 |
+
":type:": "<class 'numpy.ndarray'>",
|
60 |
+
":serialized:": "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"
|
61 |
+
},
|
62 |
+
"_last_episode_starts": {
|
63 |
+
":type:": "<class 'numpy.ndarray'>",
|
64 |
+
":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="
|
65 |
+
},
|
66 |
+
"_last_original_obs": null,
|
67 |
+
"_episode_num": 0,
|
68 |
+
"use_sde": false,
|
69 |
+
"sde_sample_freq": -1,
|
70 |
+
"_current_progress_remaining": -0.1468799999999999,
|
71 |
+
"ep_info_buffer": {
|
72 |
+
":type:": "<class 'collections.deque'>",
|
73 |
+
":serialized:": "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"
|
74 |
+
},
|
75 |
+
"ep_success_buffer": {
|
76 |
+
":type:": "<class 'collections.deque'>",
|
77 |
+
":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
|
78 |
+
},
|
79 |
+
"_n_updates": 70,
|
80 |
+
"n_steps": 2048,
|
81 |
+
"gamma": 0.99,
|
82 |
+
"gae_lambda": 0.95,
|
83 |
+
"ent_coef": 0.0,
|
84 |
+
"vf_coef": 0.5,
|
85 |
+
"max_grad_norm": 0.5,
|
86 |
+
"batch_size": 64,
|
87 |
+
"n_epochs": 10,
|
88 |
+
"clip_range": {
|
89 |
+
":type:": "<class 'function'>",
|
90 |
+
":serialized:": "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"
|
91 |
+
},
|
92 |
+
"clip_range_vf": null,
|
93 |
+
"normalize_advantage": true,
|
94 |
+
"target_kl": null
|
95 |
+
}
|
ppo-LunarLander-v2/policy.optimizer.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:2ff91ded9b6aeb3437a8b495653ecefd8f469acc4fcb4322f8ed77e44f820dbb
|
3 |
+
size 87929
|
ppo-LunarLander-v2/policy.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:28e6d13aea6e0f6cef21ac2495a691eee012bb38bdda9368c20a61e5dcb7fba0
|
3 |
+
size 43393
|
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-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
|
2 |
+
- Python: 3.9.16
|
3 |
+
- Stable-Baselines3: 1.7.0
|
4 |
+
- PyTorch: 1.13.1+cu116
|
5 |
+
- GPU Enabled: True
|
6 |
+
- Numpy: 1.22.4
|
7 |
+
- Gym: 0.21.0
|
replay.mp4
ADDED
Binary file (264 kB). View file
|
|
results.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"mean_reward": -97.11085827615871, "std_reward": 39.314128471191204, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-13T08:24:45.662372"}
|