Hugging Face's logo

dn-gh
/
TQC-PandaReachDense-v2

Reinforcement Learning
stable-baselines3
PandaReachDense-v2
deep-reinforcement-learning
Eval Results
Model card Files Community
1
dn-gh commited on
Commit
28aa1bb
•
1 Parent(s): 1f15f64

Initial commit

Browse files
Files changed (14) hide show
  1. README.md +37 -0
  2. TQC-PandaReachDense-v2.zip +3 -0
  3. TQC-PandaReachDense-v2/_stable_baselines3_version +1 -0
  4. TQC-PandaReachDense-v2/actor.optimizer.pth +3 -0
  5. TQC-PandaReachDense-v2/critic.optimizer.pth +3 -0
  6. TQC-PandaReachDense-v2/data +111 -0
  7. TQC-PandaReachDense-v2/ent_coef_optimizer.pth +3 -0
  8. TQC-PandaReachDense-v2/policy.pth +3 -0
  9. TQC-PandaReachDense-v2/pytorch_variables.pth +3 -0
  10. TQC-PandaReachDense-v2/system_info.txt +7 -0
  11. config.json +1 -0
  12. replay.mp4 +0 -0
  13. results.json +1 -0
  14. vec_normalize.pkl +3 -0
README.md ADDED
@@ -0,0 +1,37 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ library_name: stable-baselines3
3
+ tags:
4
+ - PandaReachDense-v2
5
+ - deep-reinforcement-learning
6
+ - reinforcement-learning
7
+ - stable-baselines3
8
+ model-index:
9
+ - name: TQC
10
+ results:
11
+ - task:
12
+ type: reinforcement-learning
13
+ name: reinforcement-learning
14
+ dataset:
15
+ name: PandaReachDense-v2
16
+ type: PandaReachDense-v2
17
+ metrics:
18
+ - type: mean_reward
19
+ value: -0.21 +/- 0.11
20
+ name: mean_reward
21
+ verified: false
22
+ ---
23
+
24
+ # **TQC** Agent playing **PandaReachDense-v2**
25
+ This is a trained model of a **TQC** agent playing **PandaReachDense-v2**
26
+ using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
27
+
28
+ ## Usage (with Stable-baselines3)
29
+ TODO: Add your code
30
+
31
+
32
+ ```python
33
+ from stable_baselines3 import ...
34
+ from huggingface_sb3 import load_from_hub
35
+
36
+ ...
37
+ ```
TQC-PandaReachDense-v2.zip ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:3077707caf5efecd383c7998a34b7a15384be3f4e54ee0512636e3a5ab00351c
3
+ size 3345957
TQC-PandaReachDense-v2/_stable_baselines3_version ADDED
@@ -0,0 +1 @@
 
 
1
+ 1.7.0
TQC-PandaReachDense-v2/actor.optimizer.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:04a5d039066c2a17b333b0057042fdf6e556f12329dd95314597ecb959e5f919
3
+ size 571805
TQC-PandaReachDense-v2/critic.optimizer.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:d6dbbd70c83536301db536cb8e66a86a0b49c499858b66c5d9f0253eada24bd1
3
+ size 1230585
TQC-PandaReachDense-v2/data ADDED
@@ -0,0 +1,111 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "policy_class": {
3
+ ":type:": "<class 'abc.ABCMeta'>",
4
+ ":serialized:": "gAWVMQAAAAAAAACMGHNiM19jb250cmliLnRxYy5wb2xpY2llc5SMEE11bHRpSW5wdXRQb2xpY3mUk5Qu",
5
+ "__module__": "sb3_contrib.tqc.policies",
6
+ "__doc__": "\n Policy class (with both actor and critic) for TQC.\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 use_sde: Whether to use State Dependent Exploration or not\n :param log_std_init: Initial value for the log standard deviation\n :param use_expln: Use ``expln()`` function instead of ``exp()`` when using gSDE 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 clip_mean: Clip the mean output when using gSDE to avoid numerical instability.\n :param features_extractor_class: Features extractor to use.\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 :param n_quantiles: Number of quantiles for the critic.\n :param n_critics: Number of critic networks to create.\n :param share_features_extractor: Whether to share or not the features extractor\n between the actor and the critic (this saves computation time)\n ",
7
+ "__init__": "<function MultiInputPolicy.__init__ at 0x7f9ab151cd30>",
8
+ "__abstractmethods__": "frozenset()",
9
+ "_abc_impl": "<_abc_data object at 0x7f9ab15221b0>"
10
+ },
11
+ "verbose": 1,
12
+ "policy_kwargs": {
13
+ "use_sde": false
14
+ },
15
+ "observation_space": {
16
+ ":type:": "<class 'gym.spaces.dict.Dict'>",
17
+ ":serialized:": "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",
18
+ "spaces": "OrderedDict([('achieved_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('desired_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('observation', Box([-10. -10. -10. -10. -10. -10.], [10. 10. 10. 10. 10. 10.], (6,), float32))])",
19
+ "_shape": null,
20
+ "dtype": null,
21
+ "_np_random": null
22
+ },
23
+ "action_space": {
24
+ ":type:": "<class 'gym.spaces.box.Box'>",
25
+ ":serialized:": "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",
26
+ "dtype": "float32",
27
+ "_shape": [
28
+ 3
29
+ ],
30
+ "low": "[-1. -1. -1.]",
31
+ "high": "[1. 1. 1.]",
32
+ "bounded_below": "[ True True True]",
33
+ "bounded_above": "[ True True True]",
34
+ "_np_random": "RandomState(MT19937)"
35
+ },
36
+ "n_envs": 4,
37
+ "num_timesteps": 1000000,
38
+ "_total_timesteps": 1000000,
39
+ "_num_timesteps_at_start": 0,
40
+ "seed": null,
41
+ "action_noise": null,
42
+ "start_time": 1675811808256032889,
43
+ "learning_rate": 0.0003,
44
+ "tensorboard_log": null,
45
+ "lr_schedule": {
46
+ ":type:": "<class 'function'>",
47
+ ":serialized:": "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"
48
+ },
49
+ "_last_obs": {
50
+ ":type:": "<class 'collections.OrderedDict'>",
51
+ ":serialized:": "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",
52
+ "achieved_goal": "[[ 0.43596694 -0.01338584 0.52263504]\n [ 0.43596694 -0.01338584 0.52263504]\n [ 0.43596694 -0.01338584 0.52263504]\n [ 0.43596694 -0.01338584 0.52263504]]",
53
+ "desired_goal": "[[ 1.4613651 -0.34772044 0.553742 ]\n [-0.4677488 -0.87801814 -1.6141973 ]\n [ 0.9683123 0.63135684 -0.10811599]\n [-0.04960717 0.58599013 -0.11697276]]",
54
+ "observation": "[[ 0.43596694 -0.01338584 0.52263504 0.07426314 -0.0024876 -0.00780154]\n [ 0.43596694 -0.01338584 0.52263504 0.07426314 -0.0024876 -0.00780154]\n [ 0.43596694 -0.01338584 0.52263504 0.07426314 -0.0024876 -0.00780154]\n [ 0.43596694 -0.01338584 0.52263504 0.07426314 -0.0024876 -0.00780154]]"
55
+ },
56
+ "_last_episode_starts": {
57
+ ":type:": "<class 'numpy.ndarray'>",
58
+ ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEBAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="
59
+ },
60
+ "_last_original_obs": {
61
+ ":type:": "<class 'collections.OrderedDict'>",
62
+ ":serialized:": "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",
63
+ "achieved_goal": "[[ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]]",
64
+ "desired_goal": "[[ 0.12655602 -0.02873416 0.19937362]\n [-0.04081171 -0.07480357 0.01277994]\n [ 0.08377933 0.05632281 0.14240776]\n [-0.00453422 0.05238159 0.14164546]]",
65
+ "observation": "[[ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]]"
66
+ },
67
+ "_episode_num": 20000,
68
+ "use_sde": false,
69
+ "sde_sample_freq": -1,
70
+ "_current_progress_remaining": 0.0,
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:": "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"
78
+ },
79
+ "_n_updates": 249975,
80
+ "buffer_size": 1000000,
81
+ "batch_size": 256,
82
+ "learning_starts": 100,
83
+ "tau": 0.005,
84
+ "gamma": 0.99,
85
+ "gradient_steps": 1,
86
+ "optimize_memory_usage": false,
87
+ "replay_buffer_class": {
88
+ ":type:": "<class 'abc.ABCMeta'>",
89
+ ":serialized:": "gAWVOQAAAAAAAACMIHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5idWZmZXJzlIwQRGljdFJlcGxheUJ1ZmZlcpSTlC4=",
90
+ "__module__": "stable_baselines3.common.buffers",
91
+ "__doc__": "\n Dict Replay buffer used in off-policy algorithms like SAC/TD3.\n Extends the ReplayBuffer to use dictionary observations\n\n :param buffer_size: Max number of element in the buffer\n :param observation_space: Observation space\n :param action_space: Action space\n :param device: PyTorch device\n :param n_envs: Number of parallel environments\n :param optimize_memory_usage: Enable a memory efficient variant\n Disabled for now (see https://github.com/DLR-RM/stable-baselines3/pull/243#discussion_r531535702)\n :param handle_timeout_termination: Handle timeout termination (due to timelimit)\n separately and treat the task as infinite horizon task.\n https://github.com/DLR-RM/stable-baselines3/issues/284\n ",
92
+ "__init__": "<function DictReplayBuffer.__init__ at 0x7f9ab19d1940>",
93
+ "add": "<function DictReplayBuffer.add at 0x7f9ab19d19d0>",
94
+ "sample": "<function DictReplayBuffer.sample at 0x7f9ab19d1a60>",
95
+ "_get_samples": "<function DictReplayBuffer._get_samples at 0x7f9ab19d1af0>",
96
+ "__abstractmethods__": "frozenset()",
97
+ "_abc_impl": "<_abc_data object at 0x7f9ab19d04e0>"
98
+ },
99
+ "replay_buffer_kwargs": {},
100
+ "train_freq": {
101
+ ":type:": "<class 'stable_baselines3.common.type_aliases.TrainFreq'>",
102
+ ":serialized:": "gAWVYQAAAAAAAACMJXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi50eXBlX2FsaWFzZXOUjAlUcmFpbkZyZXGUk5RLAWgAjBJUcmFpbkZyZXF1ZW5jeVVuaXSUk5SMBHN0ZXCUhZRSlIaUgZQu"
103
+ },
104
+ "use_sde_at_warmup": false,
105
+ "target_entropy": -3.0,
106
+ "ent_coef": "auto",
107
+ "target_update_interval": 1,
108
+ "top_quantiles_to_drop_per_net": 2,
109
+ "batch_norm_stats": [],
110
+ "batch_norm_stats_target": []
111
+ }
TQC-PandaReachDense-v2/ent_coef_optimizer.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:9aa9a15da08983f69c62ef701808d4e8c23d211112b4e6ee2db75b68e6577c00
3
+ size 1507
TQC-PandaReachDense-v2/policy.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:d67767f99e77bca22ac1905ed7dd749d83ba33daccdb2a3890bfaeccd2ce9b2c
3
+ size 1515205
TQC-PandaReachDense-v2/pytorch_variables.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:2f63daa7333cc510b3570b3c6a6ba1ed356d04ba27b2bc742e581ef11f77d6d7
3
+ size 747
TQC-PandaReachDense-v2/system_info.txt ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ - OS: Linux-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
2
+ - Python: 3.8.10
3
+ - Stable-Baselines3: 1.7.0
4
+ - PyTorch: 1.13.1+cu116
5
+ - GPU Enabled: True
6
+ - Numpy: 1.21.6
7
+ - Gym: 0.21.0
config.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVMQAAAAAAAACMGHNiM19jb250cmliLnRxYy5wb2xpY2llc5SMEE11bHRpSW5wdXRQb2xpY3mUk5Qu", "__module__": "sb3_contrib.tqc.policies", "__doc__": "\n Policy class (with both actor and critic) for TQC.\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 use_sde: Whether to use State Dependent Exploration or not\n :param log_std_init: Initial value for the log standard deviation\n :param use_expln: Use ``expln()`` function instead of ``exp()`` when using gSDE 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 clip_mean: Clip the mean output when using gSDE to avoid numerical instability.\n :param features_extractor_class: Features extractor to use.\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 :param n_quantiles: Number of quantiles for the critic.\n :param n_critics: Number of critic networks to create.\n :param share_features_extractor: Whether to share or not the features extractor\n between the actor and the critic (this saves computation time)\n ", "__init__": "<function MultiInputPolicy.__init__ at 0x7f9ab151cd30>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f9ab15221b0>"}, "verbose": 1, "policy_kwargs": {"use_sde": false}, "observation_space": {":type:": "<class 'gym.spaces.dict.Dict'>", ":serialized:": "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", "spaces": "OrderedDict([('achieved_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('desired_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('observation', Box([-10. -10. -10. -10. -10. -10.], [10. 10. 10. 10. 10. 10.], (6,), float32))])", "_shape": null, "dtype": null, "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [3], "low": "[-1. -1. -1.]", "high": "[1. 1. 1.]", "bounded_below": "[ True True True]", "bounded_above": "[ True True True]", "_np_random": "RandomState(MT19937)"}, "n_envs": 4, "num_timesteps": 1000000, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1675811808256032889, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/M6kqMFUyYYWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="}, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[ 0.43596694 -0.01338584 0.52263504]\n [ 0.43596694 -0.01338584 0.52263504]\n [ 0.43596694 -0.01338584 0.52263504]\n [ 0.43596694 -0.01338584 0.52263504]]", "desired_goal": "[[ 1.4613651 -0.34772044 0.553742 ]\n [-0.4677488 -0.87801814 -1.6141973 ]\n [ 0.9683123 0.63135684 -0.10811599]\n [-0.04960717 0.58599013 -0.11697276]]", "observation": "[[ 0.43596694 -0.01338584 0.52263504 0.07426314 -0.0024876 -0.00780154]\n [ 0.43596694 -0.01338584 0.52263504 0.07426314 -0.0024876 -0.00780154]\n [ 0.43596694 -0.01338584 0.52263504 0.07426314 -0.0024876 -0.00780154]\n [ 0.43596694 -0.01338584 0.52263504 0.07426314 -0.0024876 -0.00780154]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEBAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="}, "_last_original_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]]", "desired_goal": "[[ 0.12655602 -0.02873416 0.19937362]\n [-0.04081171 -0.07480357 0.01277994]\n [ 0.08377933 0.05632281 0.14240776]\n [-0.00453422 0.05238159 0.14164546]]", "observation": "[[ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]]"}, "_episode_num": 20000, "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:": "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"}, "_n_updates": 249975, "buffer_size": 1000000, "batch_size": 256, "learning_starts": 100, "tau": 0.005, "gamma": 0.99, "gradient_steps": 1, "optimize_memory_usage": false, "replay_buffer_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVOQAAAAAAAACMIHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5idWZmZXJzlIwQRGljdFJlcGxheUJ1ZmZlcpSTlC4=", "__module__": "stable_baselines3.common.buffers", "__doc__": "\n Dict Replay buffer used in off-policy algorithms like SAC/TD3.\n Extends the ReplayBuffer to use dictionary observations\n\n :param buffer_size: Max number of element in the buffer\n :param observation_space: Observation space\n :param action_space: Action space\n :param device: PyTorch device\n :param n_envs: Number of parallel environments\n :param optimize_memory_usage: Enable a memory efficient variant\n Disabled for now (see https://github.com/DLR-RM/stable-baselines3/pull/243#discussion_r531535702)\n :param handle_timeout_termination: Handle timeout termination (due to timelimit)\n separately and treat the task as infinite horizon task.\n https://github.com/DLR-RM/stable-baselines3/issues/284\n ", "__init__": "<function DictReplayBuffer.__init__ at 0x7f9ab19d1940>", "add": "<function DictReplayBuffer.add at 0x7f9ab19d19d0>", "sample": "<function DictReplayBuffer.sample at 0x7f9ab19d1a60>", "_get_samples": "<function DictReplayBuffer._get_samples at 0x7f9ab19d1af0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f9ab19d04e0>"}, "replay_buffer_kwargs": {}, "train_freq": {":type:": "<class 'stable_baselines3.common.type_aliases.TrainFreq'>", ":serialized:": "gAWVYQAAAAAAAACMJXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi50eXBlX2FsaWFzZXOUjAlUcmFpbkZyZXGUk5RLAWgAjBJUcmFpbkZyZXF1ZW5jeVVuaXSUk5SMBHN0ZXCUhZRSlIaUgZQu"}, "use_sde_at_warmup": false, "target_entropy": -3.0, "ent_coef": "auto", "target_update_interval": 1, "top_quantiles_to_drop_per_net": 2, "batch_norm_stats": [], "batch_norm_stats_target": [], "system_info": {"OS": "Linux-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.10", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}
replay.mp4 ADDED
Binary file (238 kB). View file
 
results.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"mean_reward": -0.20964387570566032, "std_reward": 0.10604512722828112, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-08T01:04:55.027289"}
vec_normalize.pkl ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:32bb9508114684f2f95eee151f114f82d99ec95c0953a0b090839d1f66ba39ef
3
+ size 3056