Upload PPO LunarLander-v2 trained agent
Browse files- README.md +1 -1
- config.json +1 -1
- ppo-LunarLander-v2.zip +2 -2
- ppo-LunarLander-v2/data +22 -22
- 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: 237.02 +/- 69.34
|
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 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 0x7f3394ac1820>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f3394ac18b0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f3394ac1940>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f3394ac19d0>", "_build": "<function ActorCriticPolicy._build at 0x7f3394ac1a60>", "forward": "<function ActorCriticPolicy.forward at 0x7f3394ac1af0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f3394ac1b80>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f3394ac1c10>", "_predict": "<function ActorCriticPolicy._predict at 0x7f3394ac1ca0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f3394ac1d30>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f3394ac1dc0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f3394ac1e50>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f3394ac33c0>"}, "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": 16384, "_total_timesteps": 10000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1680470654002193181, "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": -0.6384000000000001, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 4, "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-5.10.147+-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"}}
|
|
|
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 0x7f8d9a5f53a0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f8d9a5f5430>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f8d9a5f54c0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f8d9a5f5550>", "_build": "<function ActorCriticPolicy._build at 0x7f8d9a5f55e0>", "forward": "<function ActorCriticPolicy.forward at 0x7f8d9a5f5670>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f8d9a5f5700>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f8d9a5f5790>", "_predict": "<function ActorCriticPolicy._predict at 0x7f8d9a5f5820>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f8d9a5f58b0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f8d9a5f5940>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f8d9a5f59d0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f8d9a5f4b40>"}, "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": 1, "num_timesteps": 1000448, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1680551803107424675, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAHoSBr5ORoY/jRe+vX1v3r7jtmW+qNecPQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.00044800000000000395, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 3908, "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+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOS9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOS9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/yZmZmZmZmoWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="}, "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": "2.0.0+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "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:ebe9cd250300e0348028728a7f8b365bf8ca228b7eaec43c67e8ad0fb01d6b00
|
3 |
+
size 146746
|
ppo-LunarLander-v2/data
CHANGED
@@ -4,20 +4,20 @@
|
|
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
|
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 |
-
"extract_features": "<function ActorCriticPolicy.extract_features at
|
14 |
-
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at
|
15 |
-
"_predict": "<function ActorCriticPolicy._predict at
|
16 |
-
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at
|
17 |
-
"get_distribution": "<function ActorCriticPolicy.get_distribution at
|
18 |
-
"predict_values": "<function ActorCriticPolicy.predict_values at
|
19 |
"__abstractmethods__": "frozenset()",
|
20 |
-
"_abc_impl": "<_abc._abc_data object at
|
21 |
},
|
22 |
"verbose": 1,
|
23 |
"policy_kwargs": {},
|
@@ -42,13 +42,13 @@
|
|
42 |
"dtype": "int64",
|
43 |
"_np_random": null
|
44 |
},
|
45 |
-
"n_envs":
|
46 |
-
"num_timesteps":
|
47 |
-
"_total_timesteps":
|
48 |
"_num_timesteps_at_start": 0,
|
49 |
"seed": null,
|
50 |
"action_noise": null,
|
51 |
-
"start_time":
|
52 |
"learning_rate": 0.0003,
|
53 |
"tensorboard_log": null,
|
54 |
"lr_schedule": {
|
@@ -57,26 +57,26 @@
|
|
57 |
},
|
58 |
"_last_obs": {
|
59 |
":type:": "<class 'numpy.ndarray'>",
|
60 |
-
":serialized:": "
|
61 |
},
|
62 |
"_last_episode_starts": {
|
63 |
":type:": "<class 'numpy.ndarray'>",
|
64 |
-
":serialized:": "
|
65 |
},
|
66 |
"_last_original_obs": null,
|
67 |
"_episode_num": 0,
|
68 |
"use_sde": false,
|
69 |
"sde_sample_freq": -1,
|
70 |
-
"_current_progress_remaining": -0.
|
71 |
"ep_info_buffer": {
|
72 |
":type:": "<class 'collections.deque'>",
|
73 |
-
":serialized:": "
|
74 |
},
|
75 |
"ep_success_buffer": {
|
76 |
":type:": "<class 'collections.deque'>",
|
77 |
":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
|
78 |
},
|
79 |
-
"_n_updates":
|
80 |
"n_steps": 1024,
|
81 |
"gamma": 0.999,
|
82 |
"gae_lambda": 0.98,
|
|
|
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 0x7f8d9a5f53a0>",
|
8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f8d9a5f5430>",
|
9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f8d9a5f54c0>",
|
10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f8d9a5f5550>",
|
11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7f8d9a5f55e0>",
|
12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7f8d9a5f5670>",
|
13 |
+
"extract_features": "<function ActorCriticPolicy.extract_features at 0x7f8d9a5f5700>",
|
14 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f8d9a5f5790>",
|
15 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7f8d9a5f5820>",
|
16 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f8d9a5f58b0>",
|
17 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f8d9a5f5940>",
|
18 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7f8d9a5f59d0>",
|
19 |
"__abstractmethods__": "frozenset()",
|
20 |
+
"_abc_impl": "<_abc._abc_data object at 0x7f8d9a5f4b40>"
|
21 |
},
|
22 |
"verbose": 1,
|
23 |
"policy_kwargs": {},
|
|
|
42 |
"dtype": "int64",
|
43 |
"_np_random": null
|
44 |
},
|
45 |
+
"n_envs": 1,
|
46 |
+
"num_timesteps": 1000448,
|
47 |
+
"_total_timesteps": 1000000,
|
48 |
"_num_timesteps_at_start": 0,
|
49 |
"seed": null,
|
50 |
"action_noise": null,
|
51 |
+
"start_time": 1680551803107424675,
|
52 |
"learning_rate": 0.0003,
|
53 |
"tensorboard_log": null,
|
54 |
"lr_schedule": {
|
|
|
57 |
},
|
58 |
"_last_obs": {
|
59 |
":type:": "<class 'numpy.ndarray'>",
|
60 |
+
":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAHoSBr5ORoY/jRe+vX1v3r7jtmW+qNecPQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="
|
61 |
},
|
62 |
"_last_episode_starts": {
|
63 |
":type:": "<class 'numpy.ndarray'>",
|
64 |
+
":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="
|
65 |
},
|
66 |
"_last_original_obs": null,
|
67 |
"_episode_num": 0,
|
68 |
"use_sde": false,
|
69 |
"sde_sample_freq": -1,
|
70 |
+
"_current_progress_remaining": -0.00044800000000000395,
|
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": 3908,
|
80 |
"n_steps": 1024,
|
81 |
"gamma": 0.999,
|
82 |
"gae_lambda": 0.98,
|
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:feb4bda8c42dba2d1347a5e1f61538fede2e44825ad3da5528dea8838ef778ad
|
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 43393
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:8d9c574d0b5200c6451ca4bc6e60049c54e428079bb74539cf786922637e14d7
|
3 |
size 43393
|
ppo-LunarLander-v2/system_info.txt
CHANGED
@@ -1,7 +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:
|
5 |
- GPU Enabled: True
|
6 |
- Numpy: 1.22.4
|
7 |
- Gym: 0.21.0
|
|
|
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: 2.0.0+cu118
|
5 |
- GPU Enabled: True
|
6 |
- Numpy: 1.22.4
|
7 |
- Gym: 0.21.0
|
replay.mp4
CHANGED
Binary files a/replay.mp4 and b/replay.mp4 differ
|
|
results.json
CHANGED
@@ -1 +1 @@
|
|
1 |
-
{"mean_reward":
|
|
|
1 |
+
{"mean_reward": 237.0217691572222, "std_reward": 69.33560559556445, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-03T20:46:43.903834"}
|