Commit 2.0
Browse files- README.md +1 -1
- a2c-PandaReachDense-v2.zip +2 -2
- a2c-PandaReachDense-v2/data +10 -10
- a2c-PandaReachDense-v2/policy.optimizer.pth +1 -1
- a2c-PandaReachDense-v2/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: PandaReachDense-v2
|
| 17 |
metrics:
|
| 18 |
- type: mean_reward
|
| 19 |
-
value: -
|
| 20 |
name: mean_reward
|
| 21 |
verified: false
|
| 22 |
---
|
|
|
|
| 16 |
type: PandaReachDense-v2
|
| 17 |
metrics:
|
| 18 |
- type: mean_reward
|
| 19 |
+
value: -2.54 +/- 0.64
|
| 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:
|
| 3 |
-
size
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:87007e1a0ca4665f2e6f2eece140e10fd39a174da7462ef31588aeef64251cd0
|
| 3 |
+
size 108074
|
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
|
| 8 |
"__abstractmethods__": "frozenset()",
|
| 9 |
-
"_abc_impl": "<_abc._abc_data object at
|
| 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":
|
| 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:": "
|
| 37 |
-
"achieved_goal": "[[0.
|
| 38 |
-
"desired_goal": "[[
|
| 39 |
-
"observation": "[[0.
|
| 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:": "gAWVuwEAAAAAAACMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwSbnVtcHkuY29yZS5udW1lcmljlIwLX2Zyb21idWZmZXKUk5QoljAAAAAAAAAA6nIdPRlsGqxDI0o+6nIdPRlsGqxDI0o+6nIdPRlsGqxDI0o+6nIdPRlsGqxDI0o+lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////
|
| 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": "[[
|
| 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////
|
| 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 0x7d2801c712d0>",
|
| 8 |
"__abstractmethods__": "frozenset()",
|
| 9 |
+
"_abc_impl": "<_abc._abc_data object at 0x7d2801c6d0c0>"
|
| 10 |
},
|
| 11 |
"verbose": 1,
|
| 12 |
"policy_kwargs": {
|
|
|
|
| 24 |
"_num_timesteps_at_start": 0,
|
| 25 |
"seed": null,
|
| 26 |
"action_noise": null,
|
| 27 |
+
"start_time": 1691319561765970920,
|
| 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": "[[ 0.33216515 -0.02877271 0.5523715 ]\n [ 0.33216515 -0.02877271 0.5523715 ]\n [ 0.33216515 -0.02877271 0.5523715 ]\n [ 0.33216515 -0.02877271 0.5523715 ]]",
|
| 38 |
+
"desired_goal": "[[-0.04652955 1.4852456 -1.3798964 ]\n [ 1.3611321 -0.41437396 0.8459301 ]\n [-0.79286236 1.2029009 -0.79854625]\n [ 0.3451683 0.5749422 -0.02756802]]",
|
| 39 |
+
"observation": "[[ 0.33216515 -0.02877271 0.5523715 -0.00530206 -0.00284775 -0.01444661]\n [ 0.33216515 -0.02877271 0.5523715 -0.00530206 -0.00284775 -0.01444661]\n [ 0.33216515 -0.02877271 0.5523715 -0.00530206 -0.00284775 -0.01444661]\n [ 0.33216515 -0.02877271 0.5523715 -0.00530206 -0.00284775 -0.01444661]]"
|
| 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": "[[ 0.14952563 -0.12964006 0.24577259]\n [-0.02102494 0.01413695 0.28346497]\n [ 0.02972008 -0.14229123 0.19053026]\n [ 0.12914486 -0.14978312 0.0863616 ]]",
|
| 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:": "gAWVHRAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIe7yQDg8h+r+UhpRSlIwBbJRLMowBdJRHQKetyJXyRSx1fZQoaAZoCWgPQwjRQZdw6K35v5SGlFKUaBVLMmgWR0CnrYx77bcodX2UKGgGaAloD0MIBb8NMV5TBsCUhpRSlGgVSzJoFkdAp61Pg5zYEnV9lChoBmgJaA9DCL6G4LiM2/6/lIaUUpRoFUsyaBZHQKetEH446wN1fZQoaAZoCWgPQwit+8dCdOgEwJSGlFKUaBVLMmgWR0CnrtsH8jzJdX2UKGgGaAloD0MIpn7eVKTCBMCUhpRSlGgVSzJoFkdAp66eois4k3V9lChoBmgJaA9DCIOJP4o6EwPAlIaUUpRoFUsyaBZHQKeuYXOW0JF1fZQoaAZoCWgPQwg7HF2lu8sOwJSGlFKUaBVLMmgWR0CnriIuwosqdX2UKGgGaAloD0MIGcVyS6uBDsCUhpRSlGgVSzJoFkdAp6/hGBnSOXV9lChoBmgJaA9DCM7F3/YEyfm/lIaUUpRoFUsyaBZHQKevpIoVmBh1fZQoaAZoCWgPQwgbZ9MRwM0KwJSGlFKUaBVLMmgWR0Cnr2cTSLIgdX2UKGgGaAloD0MI32sIjsvYCcCUhpRSlGgVSzJoFkdAp68nzvqkdnV9lChoBmgJaA9DCKRRgZNtYADAlIaUUpRoFUsyaBZHQKew7DTjNpx1fZQoaAZoCWgPQwikbmdfeRDzv5SGlFKUaBVLMmgWR0CnsK/j0cwQdX2UKGgGaAloD0MIXRq/8ErS/r+UhpRSlGgVSzJoFkdAp7By17Y023V9lChoBmgJaA9DCM5Q3PEm/wHAlIaUUpRoFUsyaBZHQKewM5Lh73R1fZQoaAZoCWgPQwg2WDhJ84cAwJSGlFKUaBVLMmgWR0Cnserdepn6dX2UKGgGaAloD0MIbHpQUIrW/r+UhpRSlGgVSzJoFkdAp7Gun62v0XV9lChoBmgJaA9DCED4UKIlzwrAlIaUUpRoFUsyaBZHQKexcYiPhhp1fZQoaAZoCWgPQwietkYE4+D/v5SGlFKUaBVLMmgWR0CnsTJCBwuNdX2UKGgGaAloD0MIhJz3/3GiAMCUhpRSlGgVSzJoFkdAp7N6Jj2Ba3V9lChoBmgJaA9DCJJ6T+W0p/i/lIaUUpRoFUsyaBZHQKezPmMfigl1fZQoaAZoCWgPQwjGUbmJWtoBwJSGlFKUaBVLMmgWR0CnswJc5bQkdX2UKGgGaAloD0MI61VkdECSBMCUhpRSlGgVSzJoFkdAp7LEGJN0vHV9lChoBmgJaA9DCF6hD5axIQXAlIaUUpRoFUsyaBZHQKe1KiblRxd1fZQoaAZoCWgPQwgfgNQmTg4LwJSGlFKUaBVLMmgWR0CntO7tiQT3dX2UKGgGaAloD0MIrimQ2VnUDcCUhpRSlGgVSzJoFkdAp7SyiEg4fnV9lChoBmgJaA9DCMKE0axs/wjAlIaUUpRoFUsyaBZHQKe0c6eXiR51fZQoaAZoCWgPQwiD/Gzkuin/v5SGlFKUaBVLMmgWR0Cntsqcd5prdX2UKGgGaAloD0MIaoZUUbxK+L+UhpRSlGgVSzJoFkdAp7aO1QZXMnV9lChoBmgJaA9DCDIge737oxHAlIaUUpRoFUsyaBZHQKe2UsIVuaZ1fZQoaAZoCWgPQwhA3NWryIgKwJSGlFKUaBVLMmgWR0CnthSG8EmqdX2UKGgGaAloD0MIKuPfZ1z4CsCUhpRSlGgVSzJoFkdAp7imyon8bnV9lChoBmgJaA9DCCBdbFopBPm/lIaUUpRoFUsyaBZHQKe4a4Ajps51fZQoaAZoCWgPQwiMKy6Oyo0BwJSGlFKUaBVLMmgWR0CnuC9bPhQ4dX2UKGgGaAloD0MIEtxI2SJpBcCUhpRSlGgVSzJoFkdAp7fxDst03nV9lChoBmgJaA9DCBjrG5jcaPu/lIaUUpRoFUsyaBZHQKe6WpsGgSR1fZQoaAZoCWgPQwhClC9oIeEAwJSGlFKUaBVLMmgWR0Cnuh5jQRf4dX2UKGgGaAloD0MIQN6rVib8+r+UhpRSlGgVSzJoFkdAp7nhO8Cgb3V9lChoBmgJaA9DCEYjn1c8FQ3AlIaUUpRoFUsyaBZHQKe5ocaOxSp1fZQoaAZoCWgPQwifOetTjkn6v5SGlFKUaBVLMmgWR0Cnu2nAh0QsdX2UKGgGaAloD0MIKh+CqtHLDcCUhpRSlGgVSzJoFkdAp7stjslb/3V9lChoBmgJaA9DCKMBvAUSlPm/lIaUUpRoFUsyaBZHQKe68KKHfuV1fZQoaAZoCWgPQwiEDOTZ5ZsEwJSGlFKUaBVLMmgWR0CnurFDF6zFdX2UKGgGaAloD0MIW7bWFwlNB8CUhpRSlGgVSzJoFkdAp7xekBS1mnV9lChoBmgJaA9DCGdGPxpOGQTAlIaUUpRoFUsyaBZHQKe8ImpEQXh1fZQoaAZoCWgPQwgAAtaqXfMJwJSGlFKUaBVLMmgWR0Cnu+VcUucudX2UKGgGaAloD0MItyqJ7IPMAMCUhpRSlGgVSzJoFkdAp7umNrCWNXV9lChoBmgJaA9DCAH76NSVbwrAlIaUUpRoFUsyaBZHQKe9Yd7v5QB1fZQoaAZoCWgPQwiNgApHkEoFwJSGlFKUaBVLMmgWR0CnvSV+iJwbdX2UKGgGaAloD0MID/EPW3qUAsCUhpRSlGgVSzJoFkdAp7zoPiDM/3V9lChoBmgJaA9DCKpJ8IY0SgHAlIaUUpRoFUsyaBZHQKe8qPDHfdh1fZQoaAZoCWgPQwgGoFG69G8CwJSGlFKUaBVLMmgWR0CnvlvWpZOjdX2UKGgGaAloD0MIaD7nbtcLDcCUhpRSlGgVSzJoFkdAp74fW6K+BnV9lChoBmgJaA9DCDrrU47JYv6/lIaUUpRoFUsyaBZHQKe94fZElVt1fZQoaAZoCWgPQwgiUP2DSMYHwJSGlFKUaBVLMmgWR0CnvaLWiDdydX2UKGgGaAloD0MIIVfqWRCqBcCUhpRSlGgVSzJoFkdAp79OyLQ5WHV9lChoBmgJaA9DCERPyqSG9v2/lIaUUpRoFUsyaBZHQKe/Em3OObR1fZQoaAZoCWgPQwjc1avI6CALwJSGlFKUaBVLMmgWR0CnvtUwBYFJdX2UKGgGaAloD0MI8aFESx7vC8CUhpRSlGgVSzJoFkdAp76VpRGc4HV9lChoBmgJaA9DCAaeew+X/ATAlIaUUpRoFUsyaBZHQKfARQ40dil1fZQoaAZoCWgPQwjcgxCQL2EGwJSGlFKUaBVLMmgWR0CnwAi04R29dX2UKGgGaAloD0MIgSOBBpv6AsCUhpRSlGgVSzJoFkdAp7/LZUT+N3V9lChoBmgJaA9DCNLHfECgcwjAlIaUUpRoFUsyaBZHQKe/i9q1w5x1fZQoaAZoCWgPQwgwRiQKLWsEwJSGlFKUaBVLMmgWR0CnwUXKji4sdX2UKGgGaAloD0MIVaLsLeUcCMCUhpRSlGgVSzJoFkdAp8EJTl1bJXV9lChoBmgJaA9DCPnaM0sCdALAlIaUUpRoFUsyaBZHQKfAy9/z8P51fZQoaAZoCWgPQwhjtfl/1fEDwJSGlFKUaBVLMmgWR0CnwIx//echdX2UKGgGaAloD0MI34lZL4aSBMCUhpRSlGgVSzJoFkdAp8I2F6AvtnV9lChoBmgJaA9DCJW2uMZnUgTAlIaUUpRoFUsyaBZHQKfB+dz4k/t1fZQoaAZoCWgPQwihLHx9rWsCwJSGlFKUaBVLMmgWR0Cnwb0kGA09dX2UKGgGaAloD0MIXd2x2CbVAcCUhpRSlGgVSzJoFkdAp8F+TFERa3V9lChoBmgJaA9DCMR4zas6CwPAlIaUUpRoFUsyaBZHQKfDJru6VdJ1fZQoaAZoCWgPQwhWf4RhwPIQwJSGlFKUaBVLMmgWR0CnwuophF3IdX2UKGgGaAloD0MIjSWsjbGzA8CUhpRSlGgVSzJoFkdAp8Ks4PwuunV9lChoBmgJaA9DCIY7F0Z68QbAlIaUUpRoFUsyaBZHQKfCbWV/tpp1fZQoaAZoCWgPQwjY17rUCB0BwJSGlFKUaBVLMmgWR0CnxBxZU1htdX2UKGgGaAloD0MIXwg57/+DCcCUhpRSlGgVSzJoFkdAp8PgFxGUfXV9lChoBmgJaA9DCAIOoUrNThDAlIaUUpRoFUsyaBZHQKfDot1ZDAt1fZQoaAZoCWgPQwhJTbuYZloBwJSGlFKUaBVLMmgWR0Cnw2NL+PzWdX2UKGgGaAloD0MIlPjcCfZfAcCUhpRSlGgVSzJoFkdAp8UIod+5OXV9lChoBmgJaA9DCBcplIWvjwXAlIaUUpRoFUsyaBZHQKfEzEpAlfJ1fZQoaAZoCWgPQwhEhlW8kfkFwJSGlFKUaBVLMmgWR0CnxI71AZ88dX2UKGgGaAloD0MIo6zfTExXBcCUhpRSlGgVSzJoFkdAp8RPsPatcXV9lChoBmgJaA9DCKPp7GRw1AXAlIaUUpRoFUsyaBZHQKfGDBv73wl1fZQoaAZoCWgPQwjZ6nJKQIz7v5SGlFKUaBVLMmgWR0Cnxc/CqIacdX2UKGgGaAloD0MIt9WsM74PEMCUhpRSlGgVSzJoFkdAp8WSqEOAiHV9lChoBmgJaA9DCKgeaXBbmwbAlIaUUpRoFUsyaBZHQKfFU10knkV1fZQoaAZoCWgPQwgSpFLsaPwCwJSGlFKUaBVLMmgWR0CnxyDTa0x/dX2UKGgGaAloD0MI/S/XogUo+L+UhpRSlGgVSzJoFkdAp8bkj/uLJnV9lChoBmgJaA9DCAPPvYdLTgzAlIaUUpRoFUsyaBZHQKfGp8Aq/dt1fZQoaAZoCWgPQwjp8BDGT0MGwJSGlFKUaBVLMmgWR0Cnxmiliz9kdX2UKGgGaAloD0MIcmpnmNpyCcCUhpRSlGgVSzJoFkdAp8gxyhi9ZnV9lChoBmgJaA9DCO2cZoF2ZwbAlIaUUpRoFUsyaBZHQKfH9Zdv8651fZQoaAZoCWgPQwiPxMvTuSIBwJSGlFKUaBVLMmgWR0Cnx7iCrcTKdX2UKGgGaAloD0MIRwVOtoGbDsCUhpRSlGgVSzJoFkdAp8d5L26ClXV9lChoBmgJaA9DCMh9q3Xi8gbAlIaUUpRoFUsyaBZHQKfJP48EFGJ1fZQoaAZoCWgPQwgZ/z7jwkH7v5SGlFKUaBVLMmgWR0CnyQN5MURGdX2UKGgGaAloD0MIHvruVpao+r+UhpRSlGgVSzJoFkdAp8jGWdEsrnV9lChoBmgJaA9DCGAGY0SicBLAlIaUUpRoFUsyaBZHQKfIh1M/QjV1ZS4="
|
| 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:
|
| 3 |
size 44734
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:9eb6082e6ff65f5676aad3f5f6cb04ebe559dc33658f97f96c13ecd805187706
|
| 3 |
size 44734
|
a2c-PandaReachDense-v2/policy.pth
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 46014
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:53aa9135c6b73e7d215950bdd13f2a4d807dcfc964b9494b9dabe8bfd2ec7b28
|
| 3 |
size 46014
|
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 0x7cf792b2c940>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7cf792b25000>"}, "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": 1691263705250052373, "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": "[[0.4056012 0.00554234 0.54541093]\n [0.4056012 0.00554234 0.54541093]\n [0.4056012 0.00554234 0.54541093]\n [0.4056012 0.00554234 0.54541093]]", "desired_goal": "[[ 0.46240786 0.582648 -0.48169318]\n [ 0.47102848 -0.9179608 -1.0247215 ]\n [-1.396743 0.07874797 -1.4120388 ]\n [-1.5068595 0.3750112 0.25422946]]", "observation": "[[0.4056012 0.00554234 0.54541093 0.01480405 0.00075944 0.01234206]\n [0.4056012 0.00554234 0.54541093 0.01480405 0.00075944 0.01234206]\n [0.4056012 0.00554234 0.54541093 0.01480405 0.00075944 0.01234206]\n [0.4056012 0.00554234 0.54541093 0.01480405 0.00075944 0.01234206]]"}, "_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": "[[-0.05412992 -0.11561424 0.21261217]\n [-0.00469049 -0.02311341 0.09854712]\n [-0.1360522 0.08481427 0.02998248]\n [ 0.12737647 -0.04894466 0.08566415]]", "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": "3.10.12", "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 0x7d2801c712d0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7d2801c6d0c0>"}, "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": 1691319561765970920, "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": "[[ 0.33216515 -0.02877271 0.5523715 ]\n [ 0.33216515 -0.02877271 0.5523715 ]\n [ 0.33216515 -0.02877271 0.5523715 ]\n [ 0.33216515 -0.02877271 0.5523715 ]]", "desired_goal": "[[-0.04652955 1.4852456 -1.3798964 ]\n [ 1.3611321 -0.41437396 0.8459301 ]\n [-0.79286236 1.2029009 -0.79854625]\n [ 0.3451683 0.5749422 -0.02756802]]", "observation": "[[ 0.33216515 -0.02877271 0.5523715 -0.00530206 -0.00284775 -0.01444661]\n [ 0.33216515 -0.02877271 0.5523715 -0.00530206 -0.00284775 -0.01444661]\n [ 0.33216515 -0.02877271 0.5523715 -0.00530206 -0.00284775 -0.01444661]\n [ 0.33216515 -0.02877271 0.5523715 -0.00530206 -0.00284775 -0.01444661]]"}, "_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": "[[ 0.14952563 -0.12964006 0.24577259]\n [-0.02102494 0.01413695 0.28346497]\n [ 0.02972008 -0.14229123 0.19053026]\n [ 0.12914486 -0.14978312 0.0863616 ]]", "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": "3.10.12", "Stable-Baselines3": "1.8.0", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "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": -
|
|
|
|
| 1 |
+
{"mean_reward": -2.536508394777775, "std_reward": 0.6364829386637959, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-08-06T11:53:17.403500"}
|
vec_normalize.pkl
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 2387
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:cf6e57a4ce32db1054d80057e0bc2e01a53081d15cf565b3e9633070ed69cf52
|
| 3 |
size 2387
|