Initial commit
Browse files- README.md +1 -1
- a2c-AntBulletEnv-v0.zip +1 -1
- a2c-AntBulletEnv-v0/data +19 -19
- a2c-AntBulletEnv-v0/policy.optimizer.pth +1 -1
- a2c-AntBulletEnv-v0/policy.pth +1 -1
- config.json +1 -1
- replay.mp4 +0 -0
- results.json +1 -1
- vec_normalize.pkl +1 -1
README.md
CHANGED
@@ -16,7 +16,7 @@ model-index:
|
|
16 |
type: AntBulletEnv-v0
|
17 |
metrics:
|
18 |
- type: mean_reward
|
19 |
-
value:
|
20 |
name: mean_reward
|
21 |
verified: false
|
22 |
---
|
|
|
16 |
type: AntBulletEnv-v0
|
17 |
metrics:
|
18 |
- type: mean_reward
|
19 |
+
value: 2033.75 +/- 14.55
|
20 |
name: mean_reward
|
21 |
verified: false
|
22 |
---
|
a2c-AntBulletEnv-v0.zip
CHANGED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
size 124941
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:db3f73e13a020a43170e506523f4e1327ecc2b79c24e93c59aa239390a0b4265
|
3 |
size 124941
|
a2c-AntBulletEnv-v0/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": {
|
@@ -62,16 +62,16 @@
|
|
62 |
"_num_timesteps_at_start": 0,
|
63 |
"seed": null,
|
64 |
"action_noise": null,
|
65 |
-
"start_time":
|
66 |
-
"learning_rate": 0.
|
67 |
"tensorboard_log": null,
|
68 |
"lr_schedule": {
|
69 |
":type:": "<class 'function'>",
|
70 |
-
":serialized:": "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
|
71 |
},
|
72 |
"_last_obs": {
|
73 |
":type:": "<class 'numpy.ndarray'>",
|
74 |
-
":serialized:": "
|
75 |
},
|
76 |
"_last_episode_starts": {
|
77 |
":type:": "<class 'numpy.ndarray'>",
|
@@ -79,7 +79,7 @@
|
|
79 |
},
|
80 |
"_last_original_obs": {
|
81 |
":type:": "<class 'numpy.ndarray'>",
|
82 |
-
":serialized:": "
|
83 |
},
|
84 |
"_episode_num": 0,
|
85 |
"use_sde": false,
|
@@ -87,7 +87,7 @@
|
|
87 |
"_current_progress_remaining": 0.0,
|
88 |
"ep_info_buffer": {
|
89 |
":type:": "<class 'collections.deque'>",
|
90 |
-
":serialized:": "
|
91 |
},
|
92 |
"ep_success_buffer": {
|
93 |
":type:": "<class 'collections.deque'>",
|
|
|
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 0x7fe3b4d30af0>",
|
8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fe3b4d30b80>",
|
9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fe3b4d30c10>",
|
10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fe3b4d30ca0>",
|
11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7fe3b4d30d30>",
|
12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7fe3b4d30dc0>",
|
13 |
+
"extract_features": "<function ActorCriticPolicy.extract_features at 0x7fe3b4d30e50>",
|
14 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fe3b4d30ee0>",
|
15 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7fe3b4d30f70>",
|
16 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fe3b4cb4040>",
|
17 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fe3b4cb40d0>",
|
18 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7fe3b4cb4160>",
|
19 |
"__abstractmethods__": "frozenset()",
|
20 |
+
"_abc_impl": "<_abc._abc_data object at 0x7fe3b4cb2b00>"
|
21 |
},
|
22 |
"verbose": 1,
|
23 |
"policy_kwargs": {
|
|
|
62 |
"_num_timesteps_at_start": 0,
|
63 |
"seed": null,
|
64 |
"action_noise": null,
|
65 |
+
"start_time": 1680686339058886538,
|
66 |
+
"learning_rate": 0.0001,
|
67 |
"tensorboard_log": null,
|
68 |
"lr_schedule": {
|
69 |
":type:": "<class 'function'>",
|
70 |
+
":serialized:": "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"
|
71 |
},
|
72 |
"_last_obs": {
|
73 |
":type:": "<class 'numpy.ndarray'>",
|
74 |
+
":serialized:": "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"
|
75 |
},
|
76 |
"_last_episode_starts": {
|
77 |
":type:": "<class 'numpy.ndarray'>",
|
|
|
79 |
},
|
80 |
"_last_original_obs": {
|
81 |
":type:": "<class 'numpy.ndarray'>",
|
82 |
+
":serialized:": "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"
|
83 |
},
|
84 |
"_episode_num": 0,
|
85 |
"use_sde": false,
|
|
|
87 |
"_current_progress_remaining": 0.0,
|
88 |
"ep_info_buffer": {
|
89 |
":type:": "<class 'collections.deque'>",
|
90 |
+
":serialized:": "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"
|
91 |
},
|
92 |
"ep_success_buffer": {
|
93 |
":type:": "<class 'collections.deque'>",
|
a2c-AntBulletEnv-v0/policy.optimizer.pth
CHANGED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
size 54078
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:2937825b7d18f0a5822798fff43f06602fea7b8e23e75dc3d3af254031fcd52d
|
3 |
size 54078
|
a2c-AntBulletEnv-v0/policy.pth
CHANGED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
size 54846
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:ac6889fe7498344874121978e98d3a238a4130402ef840456de149248dde98c0
|
3 |
size 54846
|
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 0x7fc1ccef8af0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fc1ccef8b80>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fc1ccef8c10>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fc1ccef8ca0>", "_build": "<function ActorCriticPolicy._build at 0x7fc1ccef8d30>", "forward": "<function ActorCriticPolicy.forward at 0x7fc1ccef8dc0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fc1ccef8e50>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fc1ccef8ee0>", "_predict": "<function ActorCriticPolicy._predict at 0x7fc1ccef8f70>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fc1cce96040>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fc1cce960d0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fc1cce96160>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fc1cce93d00>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gAWVgQAAAAAAAAB9lCiMD29wdGltaXplcl9jbGFzc5SME3RvcmNoLm9wdGltLnJtc3Byb3CUjAdSTVNwcm9wlJOUjBBvcHRpbWl6ZXJfa3dhcmdzlH2UKIwFYWxwaGGURz/vrhR64UeujANlcHOURz7k+LWI42jxjAx3ZWlnaHRfZGVjYXmUSwB1dS4=", "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>", "optimizer_kwargs": {"alpha": 0.99, "eps": 1e-05, "weight_decay": 0}}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [28], "low": "[-inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf]", "high": "[inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n inf inf inf inf inf inf inf inf inf inf]", "bounded_below": "[False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False]", "bounded_above": "[False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False]", "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [8], "low": "[-1. -1. -1. -1. -1. -1. -1. -1.]", "high": "[1. 1. 1. 1. 1. 1. 1. 1.]", "bounded_below": "[ True True True True True True True True]", "bounded_above": "[ True True True True True True True True]", "_np_random": null}, "n_envs": 4, "num_timesteps": 2000000, "_total_timesteps": 2000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1680649821902402963, "learning_rate": 0.0007, "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:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="}, "_last_original_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": 0.0, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 100000, "n_steps": 5, "gamma": 0.99, "gae_lambda": 1.0, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "normalize_advantage": false, "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": "False", "Numpy": "1.24.2", "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 0x7fe3b4d30af0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fe3b4d30b80>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fe3b4d30c10>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fe3b4d30ca0>", "_build": "<function ActorCriticPolicy._build at 0x7fe3b4d30d30>", "forward": "<function ActorCriticPolicy.forward at 0x7fe3b4d30dc0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fe3b4d30e50>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fe3b4d30ee0>", "_predict": "<function ActorCriticPolicy._predict at 0x7fe3b4d30f70>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fe3b4cb4040>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fe3b4cb40d0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fe3b4cb4160>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fe3b4cb2b00>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gAWVgQAAAAAAAAB9lCiMD29wdGltaXplcl9jbGFzc5SME3RvcmNoLm9wdGltLnJtc3Byb3CUjAdSTVNwcm9wlJOUjBBvcHRpbWl6ZXJfa3dhcmdzlH2UKIwFYWxwaGGURz/vrhR64UeujANlcHOURz7k+LWI42jxjAx3ZWlnaHRfZGVjYXmUSwB1dS4=", "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>", "optimizer_kwargs": {"alpha": 0.99, "eps": 1e-05, "weight_decay": 0}}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [28], "low": "[-inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf]", "high": "[inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n inf inf inf inf inf inf inf inf inf inf]", "bounded_below": "[False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False]", "bounded_above": "[False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False]", "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [8], "low": "[-1. -1. -1. -1. -1. -1. -1. -1.]", "high": "[1. 1. 1. 1. 1. 1. 1. 1.]", "bounded_below": "[ True True True True True True True True]", "bounded_above": "[ True True True True True True True True]", "_np_random": null}, "n_envs": 4, "num_timesteps": 2000000, "_total_timesteps": 2000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1680686339058886538, "learning_rate": 0.0001, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOS9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOS9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/Gjbi6xxDLYWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="}, "_last_original_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": 0.0, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 100000, "n_steps": 5, "gamma": 0.99, "gae_lambda": 1.0, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "normalize_advantage": false, "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": "False", "Numpy": "1.24.2", "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": 2033.7510381877423, "std_reward": 14.552586505844083, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-05T10:51:52.669034"}
|
vec_normalize.pkl
CHANGED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
size 2136
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:c2acccc1c0e52afe9ccf47cdd6f37957029fafa64b9f0bc318b1721cd7f86794
|
3 |
size 2136
|