Hugging Face's logo

YuvanKumar
/
a2c-PandaReachDense-v3

Reinforcement Learning
stable-baselines3
PandaReachDense-v3
deep-reinforcement-learning
Eval Results
Model card Files Community
Ankit Kumar commited on
Commit
055b4d9
•
1 Parent(s): 9a066f3

Initial commit

Browse files
Files changed (9) hide show
  1. README.md +1 -1
  2. a2c-PandaReachDense-v3.zip +2 -2
  3. a2c-PandaReachDense-v3/data +15 -15
  4. a2c-PandaReachDense-v3/policy.optimizer.pth +1 -1
  5. a2c-PandaReachDense-v3/policy.pth +1 -1
  6. config.json +1 -1
  7. replay.mp4 +0 -0
  8. results.json +1 -1
  9. vec_normalize.pkl +1 -1
README.md CHANGED
@@ -16,7 +16,7 @@ model-index:
16
  type: PandaReachDense-v3
17
  metrics:
18
  - type: mean_reward
19
- value: -0.19 +/- 0.13
20
  name: mean_reward
21
  verified: false
22
  ---
 
16
  type: PandaReachDense-v3
17
  metrics:
18
  - type: mean_reward
19
+ value: -0.22 +/- 0.12
20
  name: mean_reward
21
  verified: false
22
  ---
a2c-PandaReachDense-v3.zip CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:0232bce590771bb8e86c8cdc838e894559c8e511817bc69a5c48598804902504
3
- size 111169
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:d3ed6c303b146a375761a50ab366d0388cf26366a65176c65c1b5467dd888a64
3
+ size 111229
a2c-PandaReachDense-v3/data CHANGED
@@ -4,9 +4,9 @@
4
  ":serialized:": "gAWVRQAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMG011bHRpSW5wdXRBY3RvckNyaXRpY1BvbGljeZSTlC4=",
5
  "__module__": "stable_baselines3.common.policies",
6
  "__doc__": "\n MultiInputActorClass 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 (Tuple)\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: Uses the CombinedExtractor\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 MultiInputActorCriticPolicy.__init__ at 0x7f3b88cb8430>",
8
  "__abstractmethods__": "frozenset()",
9
- "_abc_impl": "<_abc._abc_data object at 0x7f3b88ca6800>"
10
  },
11
  "verbose": 1,
12
  "policy_kwargs": {
@@ -19,30 +19,30 @@
19
  "weight_decay": 0
20
  }
21
  },
22
- "num_timesteps": 2000000,
23
- "_total_timesteps": 2000000,
24
  "_num_timesteps_at_start": 0,
25
  "seed": null,
26
  "action_noise": null,
27
- "start_time": 1699050748656966146,
28
  "learning_rate": 0.002,
29
  "tensorboard_log": null,
30
  "_last_obs": {
31
  ":type:": "<class 'collections.OrderedDict'>",
32
- ":serialized:": "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",
33
- "achieved_goal": "[[-3.0866961 1.3813198 -2.512627 ]\n [ 1.8032879 -2.360606 -0.44295955]\n [ 2.5407434 -1.2589722 1.2374346 ]\n [-1.8898153 0.8844616 0.65226823]\n [ 0.27128464 -0.00748754 0.40246853]\n [ 0.4572688 -2.3929563 0.9560991 ]\n [ 0.83018607 0.3894715 0.2583893 ]\n [-0.32601875 -0.41274747 0.3252079 ]\n [ 2.0775092 -2.1740823 1.1106356 ]\n [ 2.0103881 -2.6992278 2.8121734 ]]",
34
- "desired_goal": "[[-1.2910293 0.62156624 -0.9475911 ]\n [ 0.66702044 -1.4705433 -1.143265 ]\n [ 1.5082206 -0.22449169 -0.3000048 ]\n [-0.3843814 0.4477924 1.5682678 ]\n [ 1.123213 0.70774263 1.3499721 ]\n [ 0.28985626 -1.5311384 -0.21504685]\n [ 1.2198678 0.7739127 -1.5171707 ]\n [-1.2658501 -0.85529304 1.1342976 ]\n [ 1.5797501 -1.3032346 -0.38044563]\n [ 1.3425646 -1.6172562 0.60803866]]",
35
- "observation": "[[-3.0866961e+00 1.3813198e+00 -2.5126269e+00 -1.0578421e+00\n 1.5501415e+00 -6.2958866e-01]\n [ 1.8032879e+00 -2.3606060e+00 -4.4295955e-01 8.0511743e-01\n -8.9626396e-01 1.3356571e+00]\n [ 2.5407434e+00 -1.2589722e+00 1.2374346e+00 4.7229904e-01\n 1.7681663e-01 -5.7306802e-01]\n [-1.8898153e+00 8.8446158e-01 6.5226823e-01 9.8771644e-01\n -1.2357074e+00 2.1398516e-01]\n [ 2.7128464e-01 -7.4875378e-03 4.0246853e-01 3.5642007e-01\n -2.9592589e-03 3.4247559e-01]\n [ 4.5726880e-01 -2.3929563e+00 9.5609909e-01 -1.4280032e-01\n -7.1635288e-01 1.8278676e+00]\n [ 8.3018607e-01 3.8947150e-01 2.5838929e-01 1.3242905e+00\n 1.6015383e+00 -1.2268236e+00]\n [-3.2601875e-01 -4.1274747e-01 3.2520789e-01 -7.7680421e-01\n -1.6086271e+00 8.5862643e-01]\n [ 2.0775092e+00 -2.1740823e+00 1.1106356e+00 3.8119188e-01\n 1.6392291e+00 -2.0336828e-01]\n [ 2.0103881e+00 -2.6992278e+00 2.8121734e+00 3.9056054e-01\n -7.3885632e-01 -1.0811208e+00]]"
36
  },
37
  "_last_episode_starts": {
38
  ":type:": "<class 'numpy.ndarray'>",
39
- ":serialized:": "gAWVfQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYKAAAAAAAAAAAAAAABAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwqFlIwBQ5R0lFKULg=="
40
  },
41
  "_last_original_obs": {
42
  ":type:": "<class 'collections.OrderedDict'>",
43
- ":serialized:": "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",
44
  "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]\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]\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]]",
45
- "desired_goal": "[[ 0.08419533 -0.0258949 0.06943757]\n [-0.14459103 0.01131694 0.09934141]\n [ 0.11471539 0.02899797 0.00803335]\n [-0.13788131 0.06411009 0.20353635]\n [ 0.13701817 -0.1276624 0.1396066 ]\n [-0.03417998 -0.08675495 0.08737639]\n [ 0.03361086 -0.14465125 0.26355705]\n [ 0.01121275 0.0886263 0.2122965 ]\n [ 0.0629513 -0.06241835 0.15198451]\n [-0.138686 0.05437931 0.11185725]]",
46
  "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]\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]\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]]"
47
  },
48
  "_episode_num": 0,
@@ -52,15 +52,15 @@
52
  "_stats_window_size": 100,
53
  "ep_info_buffer": {
54
  ":type:": "<class 'collections.deque'>",
55
- ":serialized:": "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"
56
  },
57
  "ep_success_buffer": {
58
  ":type:": "<class 'collections.deque'>",
59
  ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
60
  },
61
- "_n_updates": 40000,
62
  "n_steps": 5,
63
- "gamma": 0.97,
64
  "gae_lambda": 1.0,
65
  "ent_coef": 0.0,
66
  "vf_coef": 0.5,
 
4
  ":serialized:": "gAWVRQAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMG011bHRpSW5wdXRBY3RvckNyaXRpY1BvbGljeZSTlC4=",
5
  "__module__": "stable_baselines3.common.policies",
6
  "__doc__": "\n MultiInputActorClass 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 (Tuple)\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: Uses the CombinedExtractor\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 MultiInputActorCriticPolicy.__init__ at 0x7842b4b80700>",
8
  "__abstractmethods__": "frozenset()",
9
+ "_abc_impl": "<_abc._abc_data object at 0x7842b4b7d4c0>"
10
  },
11
  "verbose": 1,
12
  "policy_kwargs": {
 
19
  "weight_decay": 0
20
  }
21
  },
22
+ "num_timesteps": 3000000,
23
+ "_total_timesteps": 3000000,
24
  "_num_timesteps_at_start": 0,
25
  "seed": null,
26
  "action_noise": null,
27
+ "start_time": 1699097429209784425,
28
  "learning_rate": 0.002,
29
  "tensorboard_log": null,
30
  "_last_obs": {
31
  ":type:": "<class 'collections.OrderedDict'>",
32
+ ":serialized:": "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",
33
+ "achieved_goal": "[[ 0.5866257 -0.40171173 0.6250874 ]\n [ 0.02814502 -1.2595319 0.74991167]\n [-0.03469753 -0.43895897 -0.11896399]\n [ 1.0128422 -1.2060119 1.1583169 ]\n [-2.2107725 -2.0085545 0.70181274]\n [ 1.0228038 1.1579473 -0.33902603]\n [ 0.29347405 -0.01958261 0.4207621 ]\n [ 0.29347405 -0.01958261 0.4207621 ]\n [-0.45600677 -0.44785932 0.34004194]\n [-1.1213344 0.7195116 -0.09829377]]",
34
+ "desired_goal": "[[ 0.25141123 -0.88212395 1.3193971 ]\n [-0.15887323 -1.0560734 1.6711498 ]\n [-0.62929326 -0.6792521 -0.6359048 ]\n [ 1.3404112 -1.0035386 1.6835921 ]\n [-1.4819919 -0.8838533 0.66487104]\n [ 0.45452604 1.3318862 0.38984433]\n [-1.1152649 -0.7198553 -0.5890924 ]\n [-0.52998716 -1.1265265 -0.8651528 ]\n [-0.7099131 -0.8164596 1.329786 ]\n [-0.95534354 0.37339467 0.14694914]]",
35
+ "observation": "[[ 5.86625695e-01 -4.01711732e-01 6.25087380e-01 1.36390030e+00\n -1.52357090e+00 1.09212148e+00]\n [ 2.81450171e-02 -1.25953186e+00 7.49911666e-01 -1.22078776e+00\n -1.06364393e+00 9.47803080e-01]\n [-3.46975289e-02 -4.38958973e-01 -1.18963994e-01 -1.60183370e+00\n -1.62103796e+00 -1.43210018e+00]\n [ 1.01284218e+00 -1.20601189e+00 1.15831685e+00 8.73830080e-01\n -9.14590180e-01 1.42309880e+00]\n [-2.21077251e+00 -2.00855446e+00 7.01812744e-01 -5.30634701e-01\n -1.55782521e+00 1.07530355e+00]\n [ 1.02280378e+00 1.15794730e+00 -3.39026034e-01 2.74106383e-01\n 7.54877210e-01 -6.82252944e-01]\n [ 2.93474048e-01 -1.95826106e-02 4.20762092e-01 3.49660605e-01\n -7.38076924e-04 3.38836163e-01]\n [ 2.93474048e-01 -1.95826106e-02 4.20762092e-01 3.49660605e-01\n -7.38076924e-04 3.38836163e-01]\n [-4.56006765e-01 -4.47859317e-01 3.40041935e-01 -7.62792468e-01\n -1.58076560e+00 8.50202799e-01]\n [-1.12133443e+00 7.19511628e-01 -9.82937738e-02 -9.49402690e-01\n -1.28237635e-01 6.20436609e-01]]"
36
  },
37
  "_last_episode_starts": {
38
  ":type:": "<class 'numpy.ndarray'>",
39
+ ":serialized:": "gAWVfQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYKAAAAAAAAAAAAAAAAAAEBAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwqFlIwBQ5R0lFKULg=="
40
  },
41
  "_last_original_obs": {
42
  ":type:": "<class 'collections.OrderedDict'>",
43
+ ":serialized:": "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",
44
  "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]\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]\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]]",
45
+ "desired_goal": "[[ 0.08538703 -0.12402468 0.06044933]\n [-0.11028957 0.125954 0.18930392]\n [ 0.12543319 -0.09416854 0.16930671]\n [-0.02343883 -0.12767239 0.1416895 ]\n [-0.08235343 0.08183837 0.2671973 ]\n [ 0.04163977 0.09952076 0.0617606 ]\n [-0.04635561 -0.0090777 0.03207151]\n [ 0.11435815 -0.00311595 0.28943047]\n [ 0.07538699 -0.09722177 0.10995796]\n [-0.08575203 0.01915823 0.04880626]]",
46
  "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]\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]\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]]"
47
  },
48
  "_episode_num": 0,
 
52
  "_stats_window_size": 100,
53
  "ep_info_buffer": {
54
  ":type:": "<class 'collections.deque'>",
55
+ ":serialized:": "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"
56
  },
57
  "ep_success_buffer": {
58
  ":type:": "<class 'collections.deque'>",
59
  ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
60
  },
61
+ "_n_updates": 60000,
62
  "n_steps": 5,
63
+ "gamma": 0.99,
64
  "gae_lambda": 1.0,
65
  "ent_coef": 0.0,
66
  "vf_coef": 0.5,
a2c-PandaReachDense-v3/policy.optimizer.pth CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:51fb7b595f9b429880385752a51de5c9849dd14c19af9cc6c2506a838185c549
3
  size 45167
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:30a61f6292f2cc025b33e980b05dc2d11d3b5d2006502a050fb0a79a196861cc
3
  size 45167
a2c-PandaReachDense-v3/policy.pth CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:d36e3a4f7d32696de8313ac356305ae104be4c2a73cd63d24050cbb0d314f23e
3
  size 46447
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:c0c542f1c7700c3042d21e43f12416420a2d089c7f8d5e725f14fbdf996ba517
3
  size 46447
config.json CHANGED
@@ -1 +1 @@
1
- {"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVRQAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMG011bHRpSW5wdXRBY3RvckNyaXRpY1BvbGljeZSTlC4=", "__module__": "stable_baselines3.common.policies", "__doc__": "\n MultiInputActorClass 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 (Tuple)\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: Uses the CombinedExtractor\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 MultiInputActorCriticPolicy.__init__ at 0x7f3b88cb8430>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f3b88ca6800>"}, "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}}, "num_timesteps": 2000000, "_total_timesteps": 2000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1699050748656966146, "learning_rate": 0.002, "tensorboard_log": null, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[-3.0866961 1.3813198 -2.512627 ]\n [ 1.8032879 -2.360606 -0.44295955]\n [ 2.5407434 -1.2589722 1.2374346 ]\n [-1.8898153 0.8844616 0.65226823]\n [ 0.27128464 -0.00748754 0.40246853]\n [ 0.4572688 -2.3929563 0.9560991 ]\n [ 0.83018607 0.3894715 0.2583893 ]\n [-0.32601875 -0.41274747 0.3252079 ]\n [ 2.0775092 -2.1740823 1.1106356 ]\n [ 2.0103881 -2.6992278 2.8121734 ]]", "desired_goal": "[[-1.2910293 0.62156624 -0.9475911 ]\n [ 0.66702044 -1.4705433 -1.143265 ]\n [ 1.5082206 -0.22449169 -0.3000048 ]\n [-0.3843814 0.4477924 1.5682678 ]\n [ 1.123213 0.70774263 1.3499721 ]\n [ 0.28985626 -1.5311384 -0.21504685]\n [ 1.2198678 0.7739127 -1.5171707 ]\n [-1.2658501 -0.85529304 1.1342976 ]\n [ 1.5797501 -1.3032346 -0.38044563]\n [ 1.3425646 -1.6172562 0.60803866]]", "observation": "[[-3.0866961e+00 1.3813198e+00 -2.5126269e+00 -1.0578421e+00\n 1.5501415e+00 -6.2958866e-01]\n [ 1.8032879e+00 -2.3606060e+00 -4.4295955e-01 8.0511743e-01\n -8.9626396e-01 1.3356571e+00]\n [ 2.5407434e+00 -1.2589722e+00 1.2374346e+00 4.7229904e-01\n 1.7681663e-01 -5.7306802e-01]\n [-1.8898153e+00 8.8446158e-01 6.5226823e-01 9.8771644e-01\n -1.2357074e+00 2.1398516e-01]\n [ 2.7128464e-01 -7.4875378e-03 4.0246853e-01 3.5642007e-01\n -2.9592589e-03 3.4247559e-01]\n [ 4.5726880e-01 -2.3929563e+00 9.5609909e-01 -1.4280032e-01\n -7.1635288e-01 1.8278676e+00]\n [ 8.3018607e-01 3.8947150e-01 2.5838929e-01 1.3242905e+00\n 1.6015383e+00 -1.2268236e+00]\n [-3.2601875e-01 -4.1274747e-01 3.2520789e-01 -7.7680421e-01\n -1.6086271e+00 8.5862643e-01]\n [ 2.0775092e+00 -2.1740823e+00 1.1106356e+00 3.8119188e-01\n 1.6392291e+00 -2.0336828e-01]\n [ 2.0103881e+00 -2.6992278e+00 2.8121734e+00 3.9056054e-01\n -7.3885632e-01 -1.0811208e+00]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVfQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYKAAAAAAAAAAAAAAABAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwqFlIwBQ5R0lFKULg=="}, "_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]\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]\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": "[[ 0.08419533 -0.0258949 0.06943757]\n [-0.14459103 0.01131694 0.09934141]\n [ 0.11471539 0.02899797 0.00803335]\n [-0.13788131 0.06411009 0.20353635]\n [ 0.13701817 -0.1276624 0.1396066 ]\n [-0.03417998 -0.08675495 0.08737639]\n [ 0.03361086 -0.14465125 0.26355705]\n [ 0.01121275 0.0886263 0.2122965 ]\n [ 0.0629513 -0.06241835 0.15198451]\n [-0.138686 0.05437931 0.11185725]]", "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]\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]\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": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": 0.0, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 40000, "n_steps": 5, "gamma": 0.97, "gae_lambda": 1.0, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "normalize_advantage": false, "observation_space": {":type:": "<class 'gymnasium.spaces.dict.Dict'>", ":serialized:": "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", "spaces": "OrderedDict([('achieved_goal', Box(-10.0, 10.0, (3,), float32)), ('desired_goal', Box(-10.0, 10.0, (3,), float32)), ('observation', Box(-10.0, 10.0, (6,), float32))])", "_shape": null, "dtype": null, "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "bounded_below": "[ True True True]", "bounded_above": "[ True True True]", "_shape": [3], "low": "[-1. -1. -1.]", "high": "[1. 1. 1.]", "low_repr": "-1.0", "high_repr": "1.0", "_np_random": null}, "n_envs": 10, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "gAWVxQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTAvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuDQwIEAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjEwL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB99lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz9gYk3S8an8hZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"}, "system_info": {"OS": "Linux-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.1.0", "PyTorch": "2.1.0+cu118", "GPU Enabled": "True", "Numpy": "1.23.5", "Cloudpickle": "2.2.1", "Gymnasium": "0.29.1", "OpenAI Gym": "0.25.2"}}
 
1
+ {"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVRQAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMG011bHRpSW5wdXRBY3RvckNyaXRpY1BvbGljeZSTlC4=", "__module__": "stable_baselines3.common.policies", "__doc__": "\n MultiInputActorClass 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 (Tuple)\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: Uses the CombinedExtractor\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 MultiInputActorCriticPolicy.__init__ at 0x7842b4b80700>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7842b4b7d4c0>"}, "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}}, "num_timesteps": 3000000, "_total_timesteps": 3000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1699097429209784425, "learning_rate": 0.002, "tensorboard_log": null, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[ 0.5866257 -0.40171173 0.6250874 ]\n [ 0.02814502 -1.2595319 0.74991167]\n [-0.03469753 -0.43895897 -0.11896399]\n [ 1.0128422 -1.2060119 1.1583169 ]\n [-2.2107725 -2.0085545 0.70181274]\n [ 1.0228038 1.1579473 -0.33902603]\n [ 0.29347405 -0.01958261 0.4207621 ]\n [ 0.29347405 -0.01958261 0.4207621 ]\n [-0.45600677 -0.44785932 0.34004194]\n [-1.1213344 0.7195116 -0.09829377]]", "desired_goal": "[[ 0.25141123 -0.88212395 1.3193971 ]\n [-0.15887323 -1.0560734 1.6711498 ]\n [-0.62929326 -0.6792521 -0.6359048 ]\n [ 1.3404112 -1.0035386 1.6835921 ]\n [-1.4819919 -0.8838533 0.66487104]\n [ 0.45452604 1.3318862 0.38984433]\n [-1.1152649 -0.7198553 -0.5890924 ]\n [-0.52998716 -1.1265265 -0.8651528 ]\n [-0.7099131 -0.8164596 1.329786 ]\n [-0.95534354 0.37339467 0.14694914]]", "observation": "[[ 5.86625695e-01 -4.01711732e-01 6.25087380e-01 1.36390030e+00\n -1.52357090e+00 1.09212148e+00]\n [ 2.81450171e-02 -1.25953186e+00 7.49911666e-01 -1.22078776e+00\n -1.06364393e+00 9.47803080e-01]\n [-3.46975289e-02 -4.38958973e-01 -1.18963994e-01 -1.60183370e+00\n -1.62103796e+00 -1.43210018e+00]\n [ 1.01284218e+00 -1.20601189e+00 1.15831685e+00 8.73830080e-01\n -9.14590180e-01 1.42309880e+00]\n [-2.21077251e+00 -2.00855446e+00 7.01812744e-01 -5.30634701e-01\n -1.55782521e+00 1.07530355e+00]\n [ 1.02280378e+00 1.15794730e+00 -3.39026034e-01 2.74106383e-01\n 7.54877210e-01 -6.82252944e-01]\n [ 2.93474048e-01 -1.95826106e-02 4.20762092e-01 3.49660605e-01\n -7.38076924e-04 3.38836163e-01]\n [ 2.93474048e-01 -1.95826106e-02 4.20762092e-01 3.49660605e-01\n -7.38076924e-04 3.38836163e-01]\n [-4.56006765e-01 -4.47859317e-01 3.40041935e-01 -7.62792468e-01\n -1.58076560e+00 8.50202799e-01]\n [-1.12133443e+00 7.19511628e-01 -9.82937738e-02 -9.49402690e-01\n -1.28237635e-01 6.20436609e-01]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVfQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYKAAAAAAAAAAAAAAAAAAEBAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwqFlIwBQ5R0lFKULg=="}, "_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]\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]\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": "[[ 0.08538703 -0.12402468 0.06044933]\n [-0.11028957 0.125954 0.18930392]\n [ 0.12543319 -0.09416854 0.16930671]\n [-0.02343883 -0.12767239 0.1416895 ]\n [-0.08235343 0.08183837 0.2671973 ]\n [ 0.04163977 0.09952076 0.0617606 ]\n [-0.04635561 -0.0090777 0.03207151]\n [ 0.11435815 -0.00311595 0.28943047]\n [ 0.07538699 -0.09722177 0.10995796]\n [-0.08575203 0.01915823 0.04880626]]", "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]\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]\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": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": 0.0, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 60000, "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, "observation_space": {":type:": "<class 'gymnasium.spaces.dict.Dict'>", ":serialized:": "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", "spaces": "OrderedDict([('achieved_goal', Box(-10.0, 10.0, (3,), float32)), ('desired_goal', Box(-10.0, 10.0, (3,), float32)), ('observation', Box(-10.0, 10.0, (6,), float32))])", "_shape": null, "dtype": null, "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "bounded_below": "[ True True True]", "bounded_above": "[ True True True]", "_shape": [3], "low": "[-1. -1. -1.]", "high": "[1. 1. 1.]", "low_repr": "-1.0", "high_repr": "1.0", "_np_random": null}, "n_envs": 10, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Linux-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.1.0", "PyTorch": "2.1.0+cu118", "GPU Enabled": "True", "Numpy": "1.23.5", "Cloudpickle": "2.2.1", "Gymnasium": "0.29.1", "OpenAI Gym": "0.25.2"}}
replay.mp4 CHANGED
Binary files a/replay.mp4 and b/replay.mp4 differ
 
results.json CHANGED
@@ -1 +1 @@
1
- {"mean_reward": -0.19479694068431855, "std_reward": 0.13228556177801246, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-11-04T00:00:22.538780"}
 
1
+ {"mean_reward": -0.2205867238342762, "std_reward": 0.12146740948982139, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-11-04T13:21:14.531582"}
vec_normalize.pkl CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:ef3344fecf364749fe5e1f6bb51ffdc6a14ea689bcb1fe72139f031e1c9d1afc
3
  size 2641
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:fe939af62a30ed9be8235665267dc3932ca9c06025782493722db506194ed404
3
  size 2641