Upload PPO LunarLander 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 +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 +1,28 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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: 239.95 +/- 17.78
|
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 0x7f1047069440>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f10470694d0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f1047069560>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f10470695f0>", "_build": "<function ActorCriticPolicy._build at 0x7f1047069680>", "forward": "<function ActorCriticPolicy.forward at 0x7f1047069710>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f10470697a0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f1047069830>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f10470698c0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f1047069950>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f10470699e0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f10470b95d0>"}, "verbose": 1, "policy_kwargs": {}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "gAWVnwEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLCIWUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWIAAAAAAAAAAAAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/5RoCksIhZSMAUOUdJRSlIwEaGlnaJRoEiiWIAAAAAAAAAAAAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf5RoCksIhZRoFXSUUpSMDWJvdW5kZWRfYmVsb3eUaBIolggAAAAAAAAAAAAAAAAAAACUaAeMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLCIWUaBV0lFKUjA1ib3VuZGVkX2Fib3ZllGgSKJYIAAAAAAAAAAAAAAAAAAAAlGghSwiFlGgVdJRSlIwKX25wX3JhbmRvbZROdWIu", "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": 507904, "_total_timesteps": 500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1651776618.4768767, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "gAWVvwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwNX2J1aWx0aW5fdHlwZZSTlIwKTGFtYmRhVHlwZZSFlFKUKGgCjAhDb2RlVHlwZZSFlFKUKEsBSwBLAUsBSxNDBIgAUwCUToWUKYwBX5SFlIxIL3Vzci9sb2NhbC9saWIvcHl0aG9uMy43L2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lIwEZnVuY5RLgEMCAAGUjAN2YWyUhZQpdJRSlH2UKIwLX19wYWNrYWdlX1+UjBhzdGFibGVfYmFzZWxpbmVzMy5jb21tb26UjAhfX25hbWVfX5SMHnN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi51dGlsc5SMCF9fZmlsZV9flIxIL3Vzci9sb2NhbC9saWIvcHl0aG9uMy43L2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaCB9lH2UKGgXaA6MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgYjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz8zqSowVTJhhZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"}, "_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": 124, "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:e91144b0c1a6002c5f22cfa0856990c82d65033ae1b8474a61378ce764f4676a
|
3 |
+
size 144044
|
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:": "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 0x7f1047069440>",
|
8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f10470694d0>",
|
9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f1047069560>",
|
10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f10470695f0>",
|
11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7f1047069680>",
|
12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7f1047069710>",
|
13 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f10470697a0>",
|
14 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7f1047069830>",
|
15 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f10470698c0>",
|
16 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f1047069950>",
|
17 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7f10470699e0>",
|
18 |
+
"__abstractmethods__": "frozenset()",
|
19 |
+
"_abc_impl": "<_abc_data object at 0x7f10470b95d0>"
|
20 |
+
},
|
21 |
+
"verbose": 1,
|
22 |
+
"policy_kwargs": {},
|
23 |
+
"observation_space": {
|
24 |
+
":type:": "<class 'gym.spaces.box.Box'>",
|
25 |
+
":serialized:": "gAWVnwEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLCIWUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWIAAAAAAAAAAAAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/5RoCksIhZSMAUOUdJRSlIwEaGlnaJRoEiiWIAAAAAAAAAAAAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf5RoCksIhZRoFXSUUpSMDWJvdW5kZWRfYmVsb3eUaBIolggAAAAAAAAAAAAAAAAAAACUaAeMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLCIWUaBV0lFKUjA1ib3VuZGVkX2Fib3ZllGgSKJYIAAAAAAAAAAAAAAAAAAAAlGghSwiFlGgVdJRSlIwKX25wX3JhbmRvbZROdWIu",
|
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": 507904,
|
46 |
+
"_total_timesteps": 500000,
|
47 |
+
"_num_timesteps_at_start": 0,
|
48 |
+
"seed": null,
|
49 |
+
"action_noise": null,
|
50 |
+
"start_time": 1651776618.4768767,
|
51 |
+
"learning_rate": 0.0003,
|
52 |
+
"tensorboard_log": null,
|
53 |
+
"lr_schedule": {
|
54 |
+
":type:": "<class 'function'>",
|
55 |
+
":serialized:": "gAWVvwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwNX2J1aWx0aW5fdHlwZZSTlIwKTGFtYmRhVHlwZZSFlFKUKGgCjAhDb2RlVHlwZZSFlFKUKEsBSwBLAUsBSxNDBIgAUwCUToWUKYwBX5SFlIxIL3Vzci9sb2NhbC9saWIvcHl0aG9uMy43L2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lIwEZnVuY5RLgEMCAAGUjAN2YWyUhZQpdJRSlH2UKIwLX19wYWNrYWdlX1+UjBhzdGFibGVfYmFzZWxpbmVzMy5jb21tb26UjAhfX25hbWVfX5SMHnN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi51dGlsc5SMCF9fZmlsZV9flIxIL3Vzci9sb2NhbC9saWIvcHl0aG9uMy43L2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaCB9lH2UKGgXaA6MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgYjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz8zqSowVTJhhZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"
|
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": 124,
|
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 sha256:78c1d21131ca808def6a1edbdba44556999c5f0af261e3d8fa71980c5867aade
|
3 |
+
size 84829
|
ppo-LunarLander-v2/policy.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:1604a61b5fc77ba0ae648f58405b83b2d1d273fd11de9172fe30c68f00af8feb
|
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 sha256:5e3818980ef81b0eac3e9e1665e65e46a15ebce2311657426613f0fa3835b39e
|
3 |
+
size 250687
|
results.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"mean_reward": 239.94609670374038, "std_reward": 17.775594885260237, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-05T19:10:46.441233"}
|