JvThunder commited on
Commit
84dc0f1
1 Parent(s): f14815e

Initial commit

Browse files
README.md CHANGED
@@ -16,7 +16,7 @@ model-index:
16
  type: PandaReachDense-v2
17
  metrics:
18
  - type: mean_reward
19
- value: -2.71 +/- 0.62
20
  name: mean_reward
21
  verified: false
22
  ---
 
16
  type: PandaReachDense-v2
17
  metrics:
18
  - type: mean_reward
19
+ value: -2.09 +/- 0.35
20
  name: mean_reward
21
  verified: false
22
  ---
a2c-PandaReachDense-v2.zip CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:dad377266740584728f62939077f598a1342cade7bd396f9f8f7a22b531ceecd
3
- size 108061
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:58f6799502da188e6c00207dc5900222ffff9f39585e4c7f502f88faf04d1320
3
+ size 107926
a2c-PandaReachDense-v2/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 0x7f238048a0e0>",
8
  "__abstractmethods__": "frozenset()",
9
- "_abc_impl": "<_abc._abc_data object at 0x7f238047b940>"
10
  },
11
  "verbose": 1,
12
  "policy_kwargs": {
@@ -24,7 +24,7 @@
24
  "_num_timesteps_at_start": 0,
25
  "seed": null,
26
  "action_noise": null,
27
- "start_time": 1689853149209632093,
28
  "learning_rate": 0.0007,
29
  "tensorboard_log": null,
30
  "lr_schedule": {
@@ -33,10 +33,10 @@
33
  },
34
  "_last_obs": {
35
  ":type:": "<class 'collections.OrderedDict'>",
36
- ":serialized:": "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",
37
- "achieved_goal": "[[0.4388423 0.03267165 0.5649473 ]\n [0.4388423 0.03267165 0.5649473 ]\n [0.4388423 0.03267165 0.5649473 ]\n [0.4388423 0.03267165 0.5649473 ]]",
38
- "desired_goal": "[[-0.56673527 -0.62186605 -1.1465862 ]\n [ 0.7154263 -1.5437249 -1.6476228 ]\n [ 1.4977953 -0.66534775 -1.0363346 ]\n [-0.45831466 0.6951903 -0.58364993]]",
39
- "observation": "[[ 0.4388423 0.03267165 0.5649473 -0.00330538 0.00350821 -0.00755057]\n [ 0.4388423 0.03267165 0.5649473 -0.00330538 0.00350821 -0.00755057]\n [ 0.4388423 0.03267165 0.5649473 -0.00330538 0.00350821 -0.00755057]\n [ 0.4388423 0.03267165 0.5649473 -0.00330538 0.00350821 -0.00755057]]"
40
  },
41
  "_last_episode_starts": {
42
  ":type:": "<class 'numpy.ndarray'>",
@@ -44,9 +44,9 @@
44
  },
45
  "_last_original_obs": {
46
  ":type:": "<class 'collections.OrderedDict'>",
47
- ":serialized:": "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",
48
  "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]]",
49
- "desired_goal": "[[-0.12410847 -0.14934665 0.27912572]\n [ 0.09235331 -0.05195512 0.09851006]\n [ 0.00464435 0.0678367 0.03151305]\n [-0.08926176 0.06306736 0.2811926 ]]",
50
  "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]]"
51
  },
52
  "_episode_num": 0,
@@ -56,7 +56,7 @@
56
  "_stats_window_size": 100,
57
  "ep_info_buffer": {
58
  ":type:": "<class 'collections.deque'>",
59
- ":serialized:": "gAWVHRAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMI0NVW7C87C8CUhpRSlIwBbJRLMowBdJRHQKUwyGvfTCt1fZQoaAZoCWgPQwhrgxPRr40DwJSGlFKUaBVLMmgWR0ClMGz7di2EdX2UKGgGaAloD0MIpp2ayw1mB8CUhpRSlGgVSzJoFkdApTAUH2RJVnV9lChoBmgJaA9DCEXxKmubIgjAlIaUUpRoFUsyaBZHQKUvuZlWfbt1fZQoaAZoCWgPQwhPAptz8EwDwJSGlFKUaBVLMmgWR0ClMj9BBzFNdX2UKGgGaAloD0MIh1ClZg9UBMCUhpRSlGgVSzJoFkdApTHkNe+mFnV9lChoBmgJaA9DCOQSRx6I7AXAlIaUUpRoFUsyaBZHQKUxi4Cp3ot1fZQoaAZoCWgPQwgVPIVcqUcPwJSGlFKUaBVLMmgWR0ClMTE2xY7rdX2UKGgGaAloD0MIBrmLMEV5AcCUhpRSlGgVSzJoFkdApTPppJwsG3V9lChoBmgJaA9DCKBU+3Q8hgLAlIaUUpRoFUsyaBZHQKUzj5mAbyZ1fZQoaAZoCWgPQwgn2H+dm7YUwJSGlFKUaBVLMmgWR0ClMzbRnezldX2UKGgGaAloD0MIiGh0B7GzA8CUhpRSlGgVSzJoFkdApTLcbvPTonV9lChoBmgJaA9DCPsgy4KJ/wPAlIaUUpRoFUsyaBZHQKU1EpiI+GJ1fZQoaAZoCWgPQwjwUBToE7kKwJSGlFKUaBVLMmgWR0ClNLaZpi7TdX2UKGgGaAloD0MIkpT0MLRaAsCUhpRSlGgVSzJoFkdApTRdS4vvjXV9lChoBmgJaA9DCPhtiPGatwvAlIaUUpRoFUsyaBZHQKU0AkOZssR1fZQoaAZoCWgPQwhGe7yQDq8LwJSGlFKUaBVLMmgWR0ClNfZIH1OCdX2UKGgGaAloD0MIKgMHtHTlBsCUhpRSlGgVSzJoFkdApTWaV6eGwnV9lChoBmgJaA9DCGxblNkg8wPAlIaUUpRoFUsyaBZHQKU1QPwNLDh1fZQoaAZoCWgPQwjt8NdkjRoJwJSGlFKUaBVLMmgWR0ClNOYTCcgAdX2UKGgGaAloD0MIhuelYmMeBcCUhpRSlGgVSzJoFkdApTbV6HCXQnV9lChoBmgJaA9DCP/PYb68AAvAlIaUUpRoFUsyaBZHQKU2egTRIBl1fZQoaAZoCWgPQwhHqu/8ouQHwJSGlFKUaBVLMmgWR0ClNiCnHeabdX2UKGgGaAloD0MIYtnMIakVEcCUhpRSlGgVSzJoFkdApTXFuejEenV9lChoBmgJaA9DCEEsmzkkRRDAlIaUUpRoFUsyaBZHQKU3ujRlYlp1fZQoaAZoCWgPQwj7rZ0oCekFwJSGlFKUaBVLMmgWR0ClN14xUNrkdX2UKGgGaAloD0MIeVxUi4iCCcCUhpRSlGgVSzJoFkdApTcEySFGonV9lChoBmgJaA9DCOF9VS5UXgXAlIaUUpRoFUsyaBZHQKU2qbkwN9Z1fZQoaAZoCWgPQwjKwWwCDMsDwJSGlFKUaBVLMmgWR0ClOKk+otL+dX2UKGgGaAloD0MIXmkZqfdUBsCUhpRSlGgVSzJoFkdApThNcQiA2HV9lChoBmgJaA9DCOv+sRAdogPAlIaUUpRoFUsyaBZHQKU39HLA57x1fZQoaAZoCWgPQwj8x0J0CDwLwJSGlFKUaBVLMmgWR0ClN5mVJL/TdX2UKGgGaAloD0MIrrzkf/JXAcCUhpRSlGgVSzJoFkdApTmG0LMLW3V9lChoBmgJaA9DCBVzEHS0av6/lIaUUpRoFUsyaBZHQKU5KubqhUR1fZQoaAZoCWgPQwjICKhwBMkQwJSGlFKUaBVLMmgWR0ClONGdqcmTdX2UKGgGaAloD0MIk3GMZI/QCsCUhpRSlGgVSzJoFkdApTh2rjo6jnV9lChoBmgJaA9DCNZSQNr/oAjAlIaUUpRoFUsyaBZHQKU6dbg0j1R1fZQoaAZoCWgPQwitNZTai4gFwJSGlFKUaBVLMmgWR0ClOhoMKCxvdX2UKGgGaAloD0MI+n/VkSNd97+UhpRSlGgVSzJoFkdApTnBLytmtnV9lChoBmgJaA9DCFkV4Saj6g3AlIaUUpRoFUsyaBZHQKU5ZmAbyYp1fZQoaAZoCWgPQwhYU1kUdnEHwJSGlFKUaBVLMmgWR0ClO1tQbdaddX2UKGgGaAloD0MIHt/eNehLB8CUhpRSlGgVSzJoFkdApTr/YjB2wHV9lChoBmgJaA9DCEFF1a90vg3AlIaUUpRoFUsyaBZHQKU6pkvK2a51fZQoaAZoCWgPQwiwWMNF7ukLwJSGlFKUaBVLMmgWR0ClOkts3yZsdX2UKGgGaAloD0MI2IFzRpSWBcCUhpRSlGgVSzJoFkdApTw5iG34K3V9lChoBmgJaA9DCAeaz7nblQXAlIaUUpRoFUsyaBZHQKU73cwg1WN1fZQoaAZoCWgPQwgWS5F8JbAMwJSGlFKUaBVLMmgWR0ClO4Rsl9jPdX2UKGgGaAloD0MIOdGuQsrPCcCUhpRSlGgVSzJoFkdApTspm03OwHV9lChoBmgJaA9DCGjon+BixQTAlIaUUpRoFUsyaBZHQKU9FdCVryl1fZQoaAZoCWgPQwgyk6gXfJoEwJSGlFKUaBVLMmgWR0ClPLncclw+dX2UKGgGaAloD0MIgVmhSPcTCMCUhpRSlGgVSzJoFkdApTxgg1WKdnV9lChoBmgJaA9DCB+/t+nP/gjAlIaUUpRoFUsyaBZHQKU8BYoRZlp1fZQoaAZoCWgPQwiOeR1xyOYPwJSGlFKUaBVLMmgWR0ClPgAckt2+dX2UKGgGaAloD0MIzvv/OGGCAcCUhpRSlGgVSzJoFkdApT2kPxx1gnV9lChoBmgJaA9DCGmOrPwyOAPAlIaUUpRoFUsyaBZHQKU9SwMYuTR1fZQoaAZoCWgPQwjZfFwbKmYGwJSGlFKUaBVLMmgWR0ClPPArxy4ndX2UKGgGaAloD0MIQZ3y6EZYAsCUhpRSlGgVSzJoFkdApT7c9yLhrHV9lChoBmgJaA9DCPqZet0isA3AlIaUUpRoFUsyaBZHQKU+gQRPGhp1fZQoaAZoCWgPQwgn+nyUEbcJwJSGlFKUaBVLMmgWR0ClPietr9EUdX2UKGgGaAloD0MIZvm6DP8JCcCUhpRSlGgVSzJoFkdApT3Mona37XV9lChoBmgJaA9DCG0eh8H8FQTAlIaUUpRoFUsyaBZHQKU/uvFm4Al1fZQoaAZoCWgPQwg3xeOiWsQFwJSGlFKUaBVLMmgWR0ClP1/HPu5SdX2UKGgGaAloD0MIjJ/GvfntCMCUhpRSlGgVSzJoFkdApT8HeizsyHV9lChoBmgJaA9DCEYiNIKNywPAlIaUUpRoFUsyaBZHQKU+rNgSey11fZQoaAZoCWgPQwhcHQBxV2/+v5SGlFKUaBVLMmgWR0ClQJx+z+m4dX2UKGgGaAloD0MIV89J7xu/C8CUhpRSlGgVSzJoFkdApUBAwyqMnHV9lChoBmgJaA9DCE0QdR+AVATAlIaUUpRoFUsyaBZHQKU/53W4EwF1fZQoaAZoCWgPQwhzvALRk7IKwJSGlFKUaBVLMmgWR0ClP4xqO939dX2UKGgGaAloD0MI8DUEx2WcAsCUhpRSlGgVSzJoFkdApUGYk3S8anV9lChoBmgJaA9DCMdjBirjnwXAlIaUUpRoFUsyaBZHQKVBPR5TqB51fZQoaAZoCWgPQwi8JM6KqGkCwJSGlFKUaBVLMmgWR0ClQOPMSsbOdX2UKGgGaAloD0MIaVIKur1EC8CUhpRSlGgVSzJoFkdApUCJHoX9BXV9lChoBmgJaA9DCL3EWKZfwgnAlIaUUpRoFUsyaBZHQKVCca3qiXZ1fZQoaAZoCWgPQwjV6NUApQECwJSGlFKUaBVLMmgWR0ClQhXCbc46dX2UKGgGaAloD0MIUaOQZFaPDsCUhpRSlGgVSzJoFkdApUG8V8CxNnV9lChoBmgJaA9DCKtCA7FsRgLAlIaUUpRoFUsyaBZHQKVBYYoAn2J1fZQoaAZoCWgPQwh1lIPZBPgFwJSGlFKUaBVLMmgWR0ClQ0pyp71JdX2UKGgGaAloD0MIPzifOlZJBMCUhpRSlGgVSzJoFkdApULufkFOf3V9lChoBmgJaA9DCBcNGY9SaQXAlIaUUpRoFUsyaBZHQKVClUBnzxx1fZQoaAZoCWgPQwjbheY6jRQNwJSGlFKUaBVLMmgWR0ClQjo371qWdX2UKGgGaAloD0MIzlSIR+Ll97+UhpRSlGgVSzJoFkdApUQs/bCaZ3V9lChoBmgJaA9DCH47iQj/ggbAlIaUUpRoFUsyaBZHQKVD0QpWmxd1fZQoaAZoCWgPQwjptdlYiXkAwJSGlFKUaBVLMmgWR0ClQ3esgdOqdX2UKGgGaAloD0MI3JvfMNHABsCUhpRSlGgVSzJoFkdApUMcqvvBrXV9lChoBmgJaA9DCO1mRj8azgfAlIaUUpRoFUsyaBZHQKVFLW7OE/V1fZQoaAZoCWgPQwg/UkSGVTwIwJSGlFKUaBVLMmgWR0ClRNGY0EX+dX2UKGgGaAloD0MIUI2XbhIjDsCUhpRSlGgVSzJoFkdApUR4UnG83HV9lChoBmgJaA9DCPj8MEJ4NADAlIaUUpRoFUsyaBZHQKVEHlZowmF1fZQoaAZoCWgPQwire2Rz1dwLwJSGlFKUaBVLMmgWR0ClRhSL61stdX2UKGgGaAloD0MIKld4l4uYBMCUhpRSlGgVSzJoFkdApUW4mNR3vHV9lChoBmgJaA9DCD52Fygp8Pu/lIaUUpRoFUsyaBZHQKVFX7WuoxZ1fZQoaAZoCWgPQwgbnfNTHAcHwJSGlFKUaBVLMmgWR0ClRQSmygPFdX2UKGgGaAloD0MI36Y/+5ECAMCUhpRSlGgVSzJoFkdApUbxd+ocaXV9lChoBmgJaA9DCK1p3nGKzgTAlIaUUpRoFUsyaBZHQKVGlY287IV1fZQoaAZoCWgPQwjAIr9+iA0DwJSGlFKUaBVLMmgWR0ClRjw+UyHmdX2UKGgGaAloD0MIRpp4B3gyCcCUhpRSlGgVSzJoFkdApUXhO8Cgb3V9lChoBmgJaA9DCN+nqtBA7AHAlIaUUpRoFUsyaBZHQKVHy2wV0tB1fZQoaAZoCWgPQwiCVfXyO43/v5SGlFKUaBVLMmgWR0ClR29qUNaydX2UKGgGaAloD0MILLmKxW9qBMCUhpRSlGgVSzJoFkdApUcWAAhjfHV9lChoBmgJaA9DCELuIkxRrgLAlIaUUpRoFUsyaBZHQKVGuvmHP/t1ZS4="
60
  },
61
  "ep_success_buffer": {
62
  ":type:": "<class 'collections.deque'>",
 
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 0x7e0050e01e10>",
8
  "__abstractmethods__": "frozenset()",
9
+ "_abc_impl": "<_abc._abc_data object at 0x7e0050e1c840>"
10
  },
11
  "verbose": 1,
12
  "policy_kwargs": {
 
24
  "_num_timesteps_at_start": 0,
25
  "seed": null,
26
  "action_noise": null,
27
+ "start_time": 1689922736507800533,
28
  "learning_rate": 0.0007,
29
  "tensorboard_log": null,
30
  "lr_schedule": {
 
33
  },
34
  "_last_obs": {
35
  ":type:": "<class 'collections.OrderedDict'>",
36
+ ":serialized:": "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",
37
+ "achieved_goal": "[[3.3853182e-01 3.8141756e-05 6.2284839e-01]\n [3.3853182e-01 3.8141756e-05 6.2284839e-01]\n [3.3853182e-01 3.8141756e-05 6.2284839e-01]\n [3.3853182e-01 3.8141756e-05 6.2284839e-01]]",
38
+ "desired_goal": "[[-1.5386606 0.66165197 0.91193753]\n [-1.6393346 -1.1179519 -0.53901553]\n [ 0.74446166 -1.6932205 -0.5720765 ]\n [ 1.4119235 -0.6448768 1.4704208 ]]",
39
+ "observation": "[[ 3.3853182e-01 3.8141756e-05 6.2284839e-01 1.4493484e-03\n -3.4191185e-03 8.7622050e-03]\n [ 3.3853182e-01 3.8141756e-05 6.2284839e-01 1.4493484e-03\n -3.4191185e-03 8.7622050e-03]\n [ 3.3853182e-01 3.8141756e-05 6.2284839e-01 1.4493484e-03\n -3.4191185e-03 8.7622050e-03]\n [ 3.3853182e-01 3.8141756e-05 6.2284839e-01 1.4493484e-03\n -3.4191185e-03 8.7622050e-03]]"
40
  },
41
  "_last_episode_starts": {
42
  ":type:": "<class 'numpy.ndarray'>",
 
44
  },
45
  "_last_original_obs": {
46
  ":type:": "<class 'collections.OrderedDict'>",
47
+ ":serialized:": "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",
48
  "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]]",
49
+ "desired_goal": "[[ 0.00101752 0.11015606 0.13207535]\n [-0.12311192 0.00386239 0.19057842]\n [ 0.01536981 0.05139211 0.2471169 ]\n [ 0.06350839 0.07997168 0.15821856]]",
50
  "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]]"
51
  },
52
  "_episode_num": 0,
 
56
  "_stats_window_size": 100,
57
  "ep_info_buffer": {
58
  ":type:": "<class 'collections.deque'>",
59
+ ":serialized:": "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"
60
  },
61
  "ep_success_buffer": {
62
  ":type:": "<class 'collections.deque'>",
a2c-PandaReachDense-v2/policy.optimizer.pth CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:11a9901856a5651bb48e1098045c655a83271b6c0bd7299309746479629c866a
3
- size 44734
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:1d622fa68d26142ac97cb7f821ee750fe6e305d30f5a2846c4bf7e1e9b165ea5
3
+ size 44606
a2c-PandaReachDense-v2/policy.pth CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:980815c3fbbfc59f5bd06967724e894fbe9e36f8f09c2533660320c513856334
3
- size 46014
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:f76563e3ddf1f9ca4f431953e16121e594f5726b4a95e16e4fb77485328b9f3d
3
+ size 45886
a2c-PandaReachDense-v2/system_info.txt CHANGED
@@ -2,6 +2,6 @@
2
  - Python: 3.10.6
3
  - Stable-Baselines3: 1.8.0
4
  - PyTorch: 2.0.1+cu118
5
- - GPU Enabled: True
6
  - Numpy: 1.22.4
7
  - Gym: 0.21.0
 
2
  - Python: 3.10.6
3
  - Stable-Baselines3: 1.8.0
4
  - PyTorch: 2.0.1+cu118
5
+ - GPU Enabled: False
6
  - Numpy: 1.22.4
7
  - Gym: 0.21.0
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 0x7f238048a0e0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f238047b940>"}, "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": 1000000, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1689853149209632093, "learning_rate": 0.0007, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[0.4388423 0.03267165 0.5649473 ]\n [0.4388423 0.03267165 0.5649473 ]\n [0.4388423 0.03267165 0.5649473 ]\n [0.4388423 0.03267165 0.5649473 ]]", "desired_goal": "[[-0.56673527 -0.62186605 -1.1465862 ]\n [ 0.7154263 -1.5437249 -1.6476228 ]\n [ 1.4977953 -0.66534775 -1.0363346 ]\n [-0.45831466 0.6951903 -0.58364993]]", "observation": "[[ 0.4388423 0.03267165 0.5649473 -0.00330538 0.00350821 -0.00755057]\n [ 0.4388423 0.03267165 0.5649473 -0.00330538 0.00350821 -0.00755057]\n [ 0.4388423 0.03267165 0.5649473 -0.00330538 0.00350821 -0.00755057]\n [ 0.4388423 0.03267165 0.5649473 -0.00330538 0.00350821 -0.00755057]]"}, "_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": "[[-0.12410847 -0.14934665 0.27912572]\n [ 0.09235331 -0.05195512 0.09851006]\n [ 0.00464435 0.0678367 0.03151305]\n [-0.08926176 0.06306736 0.2811926 ]]", "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": 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": 50000, "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 '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": null}, "n_envs": 4, "system_info": {"OS": "Linux-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023", "Python": "3.10.6", "Stable-Baselines3": "1.8.0", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}
 
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 0x7e0050e01e10>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7e0050e1c840>"}, "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": 1000000, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1689922736507800533, "learning_rate": 0.0007, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[3.3853182e-01 3.8141756e-05 6.2284839e-01]\n [3.3853182e-01 3.8141756e-05 6.2284839e-01]\n [3.3853182e-01 3.8141756e-05 6.2284839e-01]\n [3.3853182e-01 3.8141756e-05 6.2284839e-01]]", "desired_goal": "[[-1.5386606 0.66165197 0.91193753]\n [-1.6393346 -1.1179519 -0.53901553]\n [ 0.74446166 -1.6932205 -0.5720765 ]\n [ 1.4119235 -0.6448768 1.4704208 ]]", "observation": "[[ 3.3853182e-01 3.8141756e-05 6.2284839e-01 1.4493484e-03\n -3.4191185e-03 8.7622050e-03]\n [ 3.3853182e-01 3.8141756e-05 6.2284839e-01 1.4493484e-03\n -3.4191185e-03 8.7622050e-03]\n [ 3.3853182e-01 3.8141756e-05 6.2284839e-01 1.4493484e-03\n -3.4191185e-03 8.7622050e-03]\n [ 3.3853182e-01 3.8141756e-05 6.2284839e-01 1.4493484e-03\n -3.4191185e-03 8.7622050e-03]]"}, "_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": "[[ 0.00101752 0.11015606 0.13207535]\n [-0.12311192 0.00386239 0.19057842]\n [ 0.01536981 0.05139211 0.2471169 ]\n [ 0.06350839 0.07997168 0.15821856]]", "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": 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": 50000, "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 '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": null}, "n_envs": 4, "system_info": {"OS": "Linux-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023", "Python": "3.10.6", "Stable-Baselines3": "1.8.0", "PyTorch": "2.0.1+cu118", "GPU Enabled": "False", "Numpy": "1.22.4", "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": -2.7093759265262634, "std_reward": 0.62126126392664, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-07-20T12:24:28.799638"}
 
1
+ {"mean_reward": -2.0850218758452685, "std_reward": 0.3536336719589291, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-07-21T08:06:37.824000"}
vec_normalize.pkl CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:3e33c7c5de7be4f00331c486ad99d11559f24d39cf510b2f841673293c027b3a
3
  size 2387
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:57d43983e52b73100d947856afaaf799d7c20f98b6ce264bc23f96a5f8d85c4e
3
  size 2387