291 Puntos
Browse files- README.md +1 -1
- config.json +1 -1
- ppo-LunarLander-v2.zip +2 -2
- ppo-LunarLander-v2/data +23 -23
- ppo-LunarLander-v2/policy.optimizer.pth +1 -1
- ppo-LunarLander-v2/policy.pth +1 -1
- ppo-LunarLander-v2/system_info.txt +1 -1
- replay.mp4 +0 -0
- results.json +1 -1
README.md
CHANGED
@@ -16,7 +16,7 @@ model-index:
|
|
16 |
type: LunarLander-v2
|
17 |
metrics:
|
18 |
- type: mean_reward
|
19 |
-
value:
|
20 |
name: mean_reward
|
21 |
verified: false
|
22 |
---
|
|
|
16 |
type: LunarLander-v2
|
17 |
metrics:
|
18 |
- type: mean_reward
|
19 |
+
value: 283.94 +/- 17.57
|
20 |
name: mean_reward
|
21 |
verified: false
|
22 |
---
|
config.json
CHANGED
@@ -1 +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 0x7fcfead0ca60>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fcfead0caf0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fcfead0cb80>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fcfead0cc10>", "_build": "<function ActorCriticPolicy._build at 0x7fcfead0cca0>", "forward": "<function ActorCriticPolicy.forward at 0x7fcfead0cd30>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fcfead0cdc0>", "_predict": "<function ActorCriticPolicy._predict at 0x7fcfead0ce50>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fcfead0cee0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fcfead0cf70>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fcfead10040>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fcfead09510>"}, "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": 1670500204936806036, "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": 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:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4BDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/yZmZmZmZmoWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022", "Python": "3.8.15", "Stable-Baselines3": "1.6.2", "PyTorch": "1.13.0+cu116", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}
|
|
|
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 0x7ffa876b9ee0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ffa876b9f70>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ffa876bd040>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ffa876bd0d0>", "_build": "<function ActorCriticPolicy._build at 0x7ffa876bd160>", "forward": "<function ActorCriticPolicy.forward at 0x7ffa876bd1f0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ffa876bd280>", "_predict": "<function ActorCriticPolicy._predict at 0x7ffa876bd310>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ffa876bd3a0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ffa876bd430>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ffa876bd4c0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7ffa876b39c0>"}, "verbose": 2, "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": 1507328, "_total_timesteps": 1500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1670666246061433159, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4BDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/M6kqMFUyYYWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="}, "_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": -0.004885333333333408, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 1840, "n_steps": 2048, "gamma": 0.999, "gae_lambda": 0.98, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 256, "n_epochs": 8, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022", "Python": "3.8.16", "Stable-Baselines3": "1.6.2", "PyTorch": "1.13.0+cu116", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}
|
ppo-LunarLander-v2.zip
CHANGED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
-
size
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:73b99795909ae84f05f8d82079f16fd700bf0d0606f0ae0a44fea4bdc61ffedd
|
3 |
+
size 147088
|
ppo-LunarLander-v2/data
CHANGED
@@ -4,21 +4,21 @@
|
|
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
|
8 |
-
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at
|
9 |
-
"reset_noise": "<function ActorCriticPolicy.reset_noise at
|
10 |
-
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at
|
11 |
-
"_build": "<function ActorCriticPolicy._build at
|
12 |
-
"forward": "<function ActorCriticPolicy.forward at
|
13 |
-
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at
|
14 |
-
"_predict": "<function ActorCriticPolicy._predict at
|
15 |
-
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at
|
16 |
-
"get_distribution": "<function ActorCriticPolicy.get_distribution at
|
17 |
-
"predict_values": "<function ActorCriticPolicy.predict_values at
|
18 |
"__abstractmethods__": "frozenset()",
|
19 |
-
"_abc_impl": "<_abc_data object at
|
20 |
},
|
21 |
-
"verbose":
|
22 |
"policy_kwargs": {},
|
23 |
"observation_space": {
|
24 |
":type:": "<class 'gym.spaces.box.Box'>",
|
@@ -42,12 +42,12 @@
|
|
42 |
"_np_random": null
|
43 |
},
|
44 |
"n_envs": 16,
|
45 |
-
"num_timesteps":
|
46 |
-
"_total_timesteps":
|
47 |
"_num_timesteps_at_start": 0,
|
48 |
"seed": null,
|
49 |
"action_noise": null,
|
50 |
-
"start_time":
|
51 |
"learning_rate": 0.0003,
|
52 |
"tensorboard_log": null,
|
53 |
"lr_schedule": {
|
@@ -56,7 +56,7 @@
|
|
56 |
},
|
57 |
"_last_obs": {
|
58 |
":type:": "<class 'numpy.ndarray'>",
|
59 |
-
":serialized:": "
|
60 |
},
|
61 |
"_last_episode_starts": {
|
62 |
":type:": "<class 'numpy.ndarray'>",
|
@@ -66,24 +66,24 @@
|
|
66 |
"_episode_num": 0,
|
67 |
"use_sde": false,
|
68 |
"sde_sample_freq": -1,
|
69 |
-
"_current_progress_remaining": -0.
|
70 |
"ep_info_buffer": {
|
71 |
":type:": "<class 'collections.deque'>",
|
72 |
-
":serialized:": "
|
73 |
},
|
74 |
"ep_success_buffer": {
|
75 |
":type:": "<class 'collections.deque'>",
|
76 |
":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
|
77 |
},
|
78 |
-
"_n_updates":
|
79 |
-
"n_steps":
|
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":
|
86 |
-
"n_epochs":
|
87 |
"clip_range": {
|
88 |
":type:": "<class 'function'>",
|
89 |
":serialized:": "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"
|
|
|
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 0x7ffa876b9ee0>",
|
8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ffa876b9f70>",
|
9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ffa876bd040>",
|
10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ffa876bd0d0>",
|
11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7ffa876bd160>",
|
12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7ffa876bd1f0>",
|
13 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ffa876bd280>",
|
14 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7ffa876bd310>",
|
15 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ffa876bd3a0>",
|
16 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ffa876bd430>",
|
17 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7ffa876bd4c0>",
|
18 |
"__abstractmethods__": "frozenset()",
|
19 |
+
"_abc_impl": "<_abc_data object at 0x7ffa876b39c0>"
|
20 |
},
|
21 |
+
"verbose": 2,
|
22 |
"policy_kwargs": {},
|
23 |
"observation_space": {
|
24 |
":type:": "<class 'gym.spaces.box.Box'>",
|
|
|
42 |
"_np_random": null
|
43 |
},
|
44 |
"n_envs": 16,
|
45 |
+
"num_timesteps": 1507328,
|
46 |
+
"_total_timesteps": 1500000,
|
47 |
"_num_timesteps_at_start": 0,
|
48 |
"seed": null,
|
49 |
"action_noise": null,
|
50 |
+
"start_time": 1670666246061433159,
|
51 |
"learning_rate": 0.0003,
|
52 |
"tensorboard_log": null,
|
53 |
"lr_schedule": {
|
|
|
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'>",
|
|
|
66 |
"_episode_num": 0,
|
67 |
"use_sde": false,
|
68 |
"sde_sample_freq": -1,
|
69 |
+
"_current_progress_remaining": -0.004885333333333408,
|
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": 1840,
|
79 |
+
"n_steps": 2048,
|
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": 256,
|
86 |
+
"n_epochs": 8,
|
87 |
"clip_range": {
|
88 |
":type:": "<class 'function'>",
|
89 |
":serialized:": "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"
|
ppo-LunarLander-v2/policy.optimizer.pth
CHANGED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
size 87929
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:77ec1c24ace72f4c694a3426cd410ea70a0b47fe529be1ba3232aee9acfdf6d2
|
3 |
size 87929
|
ppo-LunarLander-v2/policy.pth
CHANGED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
size 43201
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:60840f02591ea5df2f4e6c696c52f5b06a05d84826683cb5bdca5ea7bb356a22
|
3 |
size 43201
|
ppo-LunarLander-v2/system_info.txt
CHANGED
@@ -1,5 +1,5 @@
|
|
1 |
OS: Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022
|
2 |
-
Python: 3.8.
|
3 |
Stable-Baselines3: 1.6.2
|
4 |
PyTorch: 1.13.0+cu116
|
5 |
GPU Enabled: True
|
|
|
1 |
OS: Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022
|
2 |
+
Python: 3.8.16
|
3 |
Stable-Baselines3: 1.6.2
|
4 |
PyTorch: 1.13.0+cu116
|
5 |
GPU Enabled: True
|
replay.mp4
CHANGED
Binary files a/replay.mp4 and b/replay.mp4 differ
|
|
results.json
CHANGED
@@ -1 +1 @@
|
|
1 |
-
{"mean_reward":
|
|
|
1 |
+
{"mean_reward": 283.94087214683043, "std_reward": 17.571357398840213, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-10T10:10:02.362900"}
|