lesliepzimmermann commited on
Commit
6d2bfd7
1 Parent(s): 92c2fd0
README.md CHANGED
@@ -16,7 +16,7 @@ model-index:
16
  type: PandaReachDense-v2
17
  metrics:
18
  - type: mean_reward
19
- value: -4.07 +/- 1.35
20
  name: mean_reward
21
  verified: false
22
  ---
 
16
  type: PandaReachDense-v2
17
  metrics:
18
  - type: mean_reward
19
+ value: -2.47 +/- 0.67
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 sha256:55d5fe325aa6fae41d2c059d3d3b9198991f0d0f38bf97edad0d2ac3011b64d6
3
- size 108146
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:29e8cc4bb67653bda6ea88769e6e26cd02cd9b1a8888d377ea570d00a2a08851
3
+ size 108075
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 0x7ef74b13a320>",
8
  "__abstractmethods__": "frozenset()",
9
- "_abc_impl": "<_abc._abc_data object at 0x7ef74b1331c0>"
10
  },
11
  "verbose": 1,
12
  "policy_kwargs": {
@@ -19,12 +19,12 @@
19
  "weight_decay": 0
20
  }
21
  },
22
- "num_timesteps": 1000000,
23
- "_total_timesteps": 1000000,
24
  "_num_timesteps_at_start": 0,
25
  "seed": null,
26
  "action_noise": null,
27
- "start_time": 1690925377078186772,
28
  "learning_rate": 0.0005,
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": "[[0.40185225 0.00811625 0.58242226]\n [0.40185225 0.00811625 0.58242226]\n [0.40185225 0.00811625 0.58242226]\n [0.40185225 0.00811625 0.58242226]]",
38
- "desired_goal": "[[-1.0688915 0.41216323 0.19991 ]\n [-1.4436307 -1.4935671 0.88269067]\n [-0.47680318 -1.4131569 -1.6043998 ]\n [ 0.7695886 -1.3873762 1.2113 ]]",
39
- "observation": "[[ 4.0185225e-01 8.1162490e-03 5.8242226e-01 -1.0681370e-04\n -7.6180423e-04 4.7093378e-03]\n [ 4.0185225e-01 8.1162490e-03 5.8242226e-01 -1.0681370e-04\n -7.6180423e-04 4.7093378e-03]\n [ 4.0185225e-01 8.1162490e-03 5.8242226e-01 -1.0681370e-04\n -7.6180423e-04 4.7093378e-03]\n [ 4.0185225e-01 8.1162490e-03 5.8242226e-01 -1.0681370e-04\n -7.6180423e-04 4.7093378e-03]]"
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": "[[-0.03426976 -0.02561021 0.13710079]\n [ 0.0813709 0.07566962 0.26503658]\n [ 0.13463503 0.07386789 0.10409405]\n [-0.05698384 -0.09592271 0.10832841]]",
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,
@@ -56,13 +56,13 @@
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'>",
63
  ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
64
  },
65
- "_n_updates": 50000,
66
  "n_steps": 5,
67
  "gamma": 0.99,
68
  "gae_lambda": 1.0,
 
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 0x7ee9b5e2bb50>",
8
  "__abstractmethods__": "frozenset()",
9
+ "_abc_impl": "<_abc._abc_data object at 0x7ee9b5e2db00>"
10
  },
11
  "verbose": 1,
12
  "policy_kwargs": {
 
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": 1691158987549319522,
28
  "learning_rate": 0.0005,
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": "[[ 0.4417113 -0.05967905 0.5380049 ]\n [ 0.4417113 -0.05967905 0.5380049 ]\n [ 0.4417113 -0.05967905 0.5380049 ]\n [ 0.4417113 -0.05967905 0.5380049 ]]",
38
+ "desired_goal": "[[-1.1248565 0.9631299 0.43385172]\n [-0.19659287 -1.2761945 0.8063217 ]\n [-0.35656226 -0.7977348 -1.6580669 ]\n [ 0.43840003 -0.4574958 -1.4289159 ]]",
39
+ "observation": "[[ 0.4417113 -0.05967905 0.5380049 -0.02144452 -0.00621986 -0.02171891]\n [ 0.4417113 -0.05967905 0.5380049 -0.02144452 -0.00621986 -0.02171891]\n [ 0.4417113 -0.05967905 0.5380049 -0.02144452 -0.00621986 -0.02171891]\n [ 0.4417113 -0.05967905 0.5380049 -0.02144452 -0.00621986 -0.02171891]]"
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": "[[ 0.00246838 0.09221012 0.19345313]\n [-0.13147342 -0.08711472 0.1186243 ]\n [ 0.08101641 -0.11890768 0.18471667]\n [-0.01098167 -0.03157875 0.10502194]]",
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'>",
63
  ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
64
  },
65
+ "_n_updates": 100000,
66
  "n_steps": 5,
67
  "gamma": 0.99,
68
  "gae_lambda": 1.0,
a2c-PandaReachDense-v2/policy.optimizer.pth CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:5adb9f397221bbba963f11ce05b2b13b3632c94a217adbbaa5c8b8f48357edbd
3
  size 44734
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:a56fa819a024fcbbb9900692142f1265310e1c67a3257057a27416a698b38ed9
3
  size 44734
a2c-PandaReachDense-v2/policy.pth CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:3c17bccb6df57b65de6205647a434f964590d124d10bafe7b49414677c9639d6
3
  size 46014
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:2daf31e9fc8b63af2291b8c63f03a7272abee156a656d560b66dd1742f79021a
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 0x7ef74b13a320>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7ef74b1331c0>"}, "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": 1690925377078186772, "learning_rate": 0.0005, "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": "[[0.40185225 0.00811625 0.58242226]\n [0.40185225 0.00811625 0.58242226]\n [0.40185225 0.00811625 0.58242226]\n [0.40185225 0.00811625 0.58242226]]", "desired_goal": "[[-1.0688915 0.41216323 0.19991 ]\n [-1.4436307 -1.4935671 0.88269067]\n [-0.47680318 -1.4131569 -1.6043998 ]\n [ 0.7695886 -1.3873762 1.2113 ]]", "observation": "[[ 4.0185225e-01 8.1162490e-03 5.8242226e-01 -1.0681370e-04\n -7.6180423e-04 4.7093378e-03]\n [ 4.0185225e-01 8.1162490e-03 5.8242226e-01 -1.0681370e-04\n -7.6180423e-04 4.7093378e-03]\n [ 4.0185225e-01 8.1162490e-03 5.8242226e-01 -1.0681370e-04\n -7.6180423e-04 4.7093378e-03]\n [ 4.0185225e-01 8.1162490e-03 5.8242226e-01 -1.0681370e-04\n -7.6180423e-04 4.7093378e-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": "[[-0.03426976 -0.02561021 0.13710079]\n [ 0.0813709 0.07566962 0.26503658]\n [ 0.13463503 0.07386789 0.10409405]\n [-0.05698384 -0.09592271 0.10832841]]", "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 0x7ee9b5e2bb50>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7ee9b5e2db00>"}, "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": 1691158987549319522, "learning_rate": 0.0005, "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": "[[ 0.4417113 -0.05967905 0.5380049 ]\n [ 0.4417113 -0.05967905 0.5380049 ]\n [ 0.4417113 -0.05967905 0.5380049 ]\n [ 0.4417113 -0.05967905 0.5380049 ]]", "desired_goal": "[[-1.1248565 0.9631299 0.43385172]\n [-0.19659287 -1.2761945 0.8063217 ]\n [-0.35656226 -0.7977348 -1.6580669 ]\n [ 0.43840003 -0.4574958 -1.4289159 ]]", "observation": "[[ 0.4417113 -0.05967905 0.5380049 -0.02144452 -0.00621986 -0.02171891]\n [ 0.4417113 -0.05967905 0.5380049 -0.02144452 -0.00621986 -0.02171891]\n [ 0.4417113 -0.05967905 0.5380049 -0.02144452 -0.00621986 -0.02171891]\n [ 0.4417113 -0.05967905 0.5380049 -0.02144452 -0.00621986 -0.02171891]]"}, "_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": "[[ 0.00246838 0.09221012 0.19345313]\n [-0.13147342 -0.08711472 0.1186243 ]\n [ 0.08101641 -0.11890768 0.18471667]\n [-0.01098167 -0.03157875 0.10502194]]", "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": 100000, "n_steps": 5, "gamma": 0.99, "gae_lambda": 1.0, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "normalize_advantage": false, "observation_space": {":type:": "<class 'gym.spaces.dict.Dict'>", ":serialized:": "gAWVWAMAAAAAAACMD2d5bS5zcGFjZXMuZGljdJSMBERpY3SUk5QpgZR9lCiMBnNwYWNlc5SMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwOZ3ltLnNwYWNlcy5ib3iUjANCb3iUk5QpgZR9lCiMBWR0eXBllIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowGX3NoYXBllEsDhZSMA2xvd5SMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYMAAAAAAAAAAAAIMEAACDBAAAgwZRoFksDhZSMAUOUdJRSlIwEaGlnaJRoHiiWDAAAAAAAAAAAACBBAAAgQQAAIEGUaBZLA4WUaCF0lFKUjA1ib3VuZGVkX2JlbG93lGgeKJYDAAAAAAAAAAEBAZRoE4wCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksDhZRoIXSUUpSMDWJvdW5kZWRfYWJvdmWUaB4olgMAAAAAAAAAAQEBlGgtSwOFlGghdJRSlIwKX25wX3JhbmRvbZROdWKMDGRlc2lyZWRfZ29hbJRoDSmBlH2UKGgQaBZoGUsDhZRoG2geKJYMAAAAAAAAAAAAIMEAACDBAAAgwZRoFksDhZRoIXSUUpRoJGgeKJYMAAAAAAAAAAAAIEEAACBBAAAgQZRoFksDhZRoIXSUUpRoKWgeKJYDAAAAAAAAAAEBAZRoLUsDhZRoIXSUUpRoM2geKJYDAAAAAAAAAAEBAZRoLUsDhZRoIXSUUpRoOE51YowLb2JzZXJ2YXRpb26UaA0pgZR9lChoEGgWaBlLBoWUaBtoHiiWGAAAAAAAAAAAACDBAAAgwQAAIMEAACDBAAAgwQAAIMGUaBZLBoWUaCF0lFKUaCRoHiiWGAAAAAAAAAAAACBBAAAgQQAAIEEAACBBAAAgQQAAIEGUaBZLBoWUaCF0lFKUaCloHiiWBgAAAAAAAAABAQEBAQGUaC1LBoWUaCF0lFKUaDNoHiiWBgAAAAAAAAABAQEBAQGUaC1LBoWUaCF0lFKUaDhOdWJ1aBlOaBBOaDhOdWIu", "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": -4.06541669962462, "std_reward": 1.3540019598410125, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-08-01T22:19:30.918179"}
 
1
+ {"mean_reward": -2.473885703133419, "std_reward": 0.666906470954311, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-08-04T15:45:27.154766"}
vec_normalize.pkl CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:d0f618850dfd79d9b3fa3e18b98fda5508ce26f6cb9d41d27266e1ff4b24794b
3
  size 2387
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:58eb2af8c30416765ea5e1bd637df3e3da077e83265056099611a62d1c685cf8
3
  size 2387