Upload PPO LunarLander-v2 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: 278.81 +/- 19.74
|
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 0x7fd4366b4e60>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fd4366b4ef0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fd4366b4f80>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fd4366bb050>", "_build": "<function ActorCriticPolicy._build at 0x7fd4366bb0e0>", "forward": "<function ActorCriticPolicy.forward at 0x7fd4366bb170>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fd4366bb200>", "_predict": "<function ActorCriticPolicy._predict at 0x7fd4366bb290>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fd4366bb320>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fd4366bb3b0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fd4366bb440>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fd4366fcc30>"}, "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:": "gAWVgQAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwFc2hhcGWUKYwFZHR5cGWUjAVudW1weZRoB5OUjAJpOJSJiIeUUpQoSwOMATyUTk5OSv////9K/////0sAdJRijApfbnBfcmFuZG9tlE51Yi4=", "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": 1651799659.2295742, "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": 248, "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": "0.17.3"}}
|
ppo-LunarLander-v2.zip
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:eb4ecd5b1a424e2cabca12ccc973d04fe9d5e0034b4884304ce22fb611b598e6
|
3 |
+
size 143980
|
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 0x7fd4366b4e60>",
|
8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fd4366b4ef0>",
|
9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fd4366b4f80>",
|
10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fd4366bb050>",
|
11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7fd4366bb0e0>",
|
12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7fd4366bb170>",
|
13 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fd4366bb200>",
|
14 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7fd4366bb290>",
|
15 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fd4366bb320>",
|
16 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fd4366bb3b0>",
|
17 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7fd4366bb440>",
|
18 |
+
"__abstractmethods__": "frozenset()",
|
19 |
+
"_abc_impl": "<_abc_data object at 0x7fd4366fcc30>"
|
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:": "gAWVgQAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwFc2hhcGWUKYwFZHR5cGWUjAVudW1weZRoB5OUjAJpOJSJiIeUUpQoSwOMATyUTk5OSv////9K/////0sAdJRijApfbnBfcmFuZG9tlE51Yi4=",
|
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": 1651799659.2295742,
|
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:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAABp0nj7Pros/w6vUPjDbJL9fm6A+LS1oPQAAAAAAAAAAgBKtPj9bZz+Ax6g+Oacivx+N0j6m9g08AAAAAAAAAAAATCU9DhCSvAhjmryPrAs9wIz6PZMX2b0AAIA/AACAP2ZDJj1ptzM99JUIvhqcB77sVC+94k+eOwAAAAAAAAAAjehoviFRFj+uLzo9kxvdvtSQE75WkgE+AAAAAAAAAACAkXM9XAN1uvqmDDpGFa41oc9vujPBJLkAAIA/AACAPxoJ4L3X3SQ8phUqvMA2EL6HPrA9Nk7EvQAAAAAAAAAAZpZSOwKytj/NZyY+nN6zPoztcLsNNhW9AAAAAAAAAAAAcAM+vdRbPDN0Uj0j1jW+TIduPUKLwrwAAAAAAAAAAGbYobxxMH+7iq/4O49tkTwp2Lg848h4vQAAgD8AAIA/M/KMPdJflLu5jjm8dFSWPHAC7Dx1x3+9AACAPwAAgD/N0ji85LCTPorHoLz785y+/UwUvAqa3z0AAAAAAAAAAE0JDj4P+XA/+PhdPkVT9b54wA8+wRoHPAAAAAAAAAAAAJ5LPOG0irrTW1uzyc3PL7Jc8DkGkMgzAACAPwAAgD9mMoe87OWpu45FJL3UDGO+2tghvLOnyL4AAIA/AACAPxpNT77f78M+E/QwPstNx76ZeYC95e7SPQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksQSwiGlIwBQ5R0lFKULg=="
|
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": 248,
|
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 sha256:6dec971eab0faaf23cdff0cd5e6417d15b1fd1bfbb33aa5452bb681e3c70068d
|
3 |
+
size 84829
|
ppo-LunarLander-v2/policy.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:489e4ff8964bcddf06c9887715b35a025bb206df6f2f756e12909dc3ac0da5c1
|
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: 0.17.3
|
replay.mp4
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:0dbab825a84187b040536d1c4a3285a388d13ce50aca4b8537e2f9d697649fda
|
3 |
+
size 214290
|
results.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"mean_reward": 278.81290612701423, "std_reward": 19.739095674636726, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-06T01:49:58.047344"}
|