SimingSiming
commited on
Commit
•
bb4244a
1
Parent(s):
f12f81b
Upload PPO LunarLander-v2 trained agent
Browse files- README.md +1 -1
- config.json +1 -1
- ppo_lunar_lander_v2.zip +2 -2
- ppo_lunar_lander_v2/data +19 -19
- ppo_lunar_lander_v2/policy.optimizer.pth +1 -1
- ppo_lunar_lander_v2/policy.pth +1 -1
- replay.mp4 +2 -2
- results.json +1 -1
README.md
CHANGED
@@ -10,7 +10,7 @@ model-index:
|
|
10 |
results:
|
11 |
- metrics:
|
12 |
- type: mean_reward
|
13 |
-
value:
|
14 |
name: mean_reward
|
15 |
task:
|
16 |
type: reinforcement-learning
|
|
|
10 |
results:
|
11 |
- metrics:
|
12 |
- type: mean_reward
|
13 |
+
value: 282.71 +/- 20.92
|
14 |
name: mean_reward
|
15 |
task:
|
16 |
type: reinforcement-learning
|
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 0x7f76678d5710>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f76678d57a0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f76678d5830>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f76678d58c0>", "_build": "<function ActorCriticPolicy._build at 0x7f76678d5950>", "forward": "<function ActorCriticPolicy.forward at 0x7f76678d59e0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f76678d5a70>", "_predict": "<function ActorCriticPolicy._predict at 0x7f76678d5b00>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f76678d5b90>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f76678d5c20>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f76678d5cb0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f7667912db0>"}, "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": 507904, "_total_timesteps": 500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1651901633.3817408, "learning_rate": 0.0001, "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": 775, "n_steps": 1024, "gamma": 0.99, "gae_lambda": 0.95, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 5, "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"}}
|
|
|
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 0x7f65985b4dd0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f65985b4e60>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f65985b4ef0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f65985b4f80>", "_build": "<function ActorCriticPolicy._build at 0x7f659853b050>", "forward": "<function ActorCriticPolicy.forward at 0x7f659853b0e0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f659853b170>", "_predict": "<function ActorCriticPolicy._predict at 0x7f659853b200>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f659853b290>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f659853b320>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f659853b3b0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f6598581a50>"}, "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": 1651910335.2434385, "learning_rate": 0.0001, "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": 496, "n_steps": 1024, "gamma": 0.999, "gae_lambda": 0.95, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 32, "n_epochs": 16, "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_lunar_lander_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:a2b8e5e744d08f01b40e1df6f144bf0bfb7dc825348cfd0d4f73becd5780b563
|
3 |
+
size 144081
|
ppo_lunar_lander_v2/data
CHANGED
@@ -4,19 +4,19 @@
|
|
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": 1,
|
22 |
"policy_kwargs": {},
|
@@ -47,7 +47,7 @@
|
|
47 |
"_num_timesteps_at_start": 0,
|
48 |
"seed": null,
|
49 |
"action_noise": null,
|
50 |
-
"start_time":
|
51 |
"learning_rate": 0.0001,
|
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'>",
|
@@ -69,21 +69,21 @@
|
|
69 |
"_current_progress_remaining": -0.015808000000000044,
|
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": 1024,
|
80 |
-
"gamma": 0.
|
81 |
"gae_lambda": 0.95,
|
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 0x7f65985b4dd0>",
|
8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f65985b4e60>",
|
9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f65985b4ef0>",
|
10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f65985b4f80>",
|
11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7f659853b050>",
|
12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7f659853b0e0>",
|
13 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f659853b170>",
|
14 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7f659853b200>",
|
15 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f659853b290>",
|
16 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f659853b320>",
|
17 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7f659853b3b0>",
|
18 |
"__abstractmethods__": "frozenset()",
|
19 |
+
"_abc_impl": "<_abc_data object at 0x7f6598581a50>"
|
20 |
},
|
21 |
"verbose": 1,
|
22 |
"policy_kwargs": {},
|
|
|
47 |
"_num_timesteps_at_start": 0,
|
48 |
"seed": null,
|
49 |
"action_noise": null,
|
50 |
+
"start_time": 1651910335.2434385,
|
51 |
"learning_rate": 0.0001,
|
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'>",
|
|
|
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": 496,
|
79 |
"n_steps": 1024,
|
80 |
+
"gamma": 0.999,
|
81 |
"gae_lambda": 0.95,
|
82 |
"ent_coef": 0.01,
|
83 |
"vf_coef": 0.5,
|
84 |
"max_grad_norm": 0.5,
|
85 |
+
"batch_size": 32,
|
86 |
+
"n_epochs": 16,
|
87 |
"clip_range": {
|
88 |
":type:": "<class 'function'>",
|
89 |
":serialized:": "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"
|
ppo_lunar_lander_v2/policy.optimizer.pth
CHANGED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
size 84893
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:57599e2ff7eebb8ae7df0f376c87a47199fd00ba062e4cf4a0d1916348f6c2b8
|
3 |
size 84893
|
ppo_lunar_lander_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:c1394825fa24ab8112a141a7e4c7e9e4aeb8980b6e2a0369e62f7731f5df87fa
|
3 |
size 43201
|
replay.mp4
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 sha256:8391361a26c928eca7f100b2e6086fcfde300692a5f62390e5e5fe0a476b297c
|
3 |
+
size 225370
|
results.json
CHANGED
@@ -1 +1 @@
|
|
1 |
-
{"mean_reward":
|
|
|
1 |
+
{"mean_reward": 282.71271120145104, "std_reward": 20.920087460894596, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-07T18:36:38.382579"}
|