double time steps; add hyperparams

README.md

@@ -16,7 +16,7 @@ model-index:
       type: PandaReachDense-v2
     metrics:
     - type: mean_reward
-      value: -4.03 +/- 0.48
+      value: -1.05 +/- 0.32
       name: mean_reward
       verified: false
 ---

a2c-PandaReachDense-v2.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:99ae6f17f28a3e8d2068bf1b10169488d45681f0dff9dac3431cf4e848f59b1f
-size 108157
+oid sha256:d01b76b0967633cf3fc6002d39aeecef8f3c0b830a7501e707c917de9c5afbc2
+size 109583

a2c-PandaReachDense-v2/data

@@ -4,14 +4,16 @@
         ":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 0x7dc7e60069e0>",
+        "__init__": "<function MultiInputActorCriticPolicy.__init__ at 0x7a695ed02e60>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x7dc7e6002900>"
+        "_abc_impl": "<_abc._abc_data object at 0x7a695ed08c00>"
     },
     "verbose": 1,
     "policy_kwargs": {
         ":type:": "<class 'dict'>",
-        ":serialized:": "gAWVgQAAAAAAAAB9lCiMD29wdGltaXplcl9jbGFzc5SME3RvcmNoLm9wdGltLnJtc3Byb3CUjAdSTVNwcm9wlJOUjBBvcHRpbWl6ZXJfa3dhcmdzlH2UKIwFYWxwaGGURz/vrhR64UeujANlcHOURz7k+LWI42jxjAx3ZWlnaHRfZGVjYXmUSwB1dS4=",
+        ":serialized:": "gAWVowAAAAAAAAB9lCiMDGxvZ19zdGRfaW5pdJRK/v///4wKb3J0aG9faW5pdJSJjA9vcHRpbWl6ZXJfY2xhc3OUjBN0b3JjaC5vcHRpbS5ybXNwcm9wlIwHUk1TcHJvcJSTlIwQb3B0aW1pemVyX2t3YXJnc5R9lCiMBWFscGhhlEc/764UeuFHrowDZXBzlEc+5Pi1iONo8YwMd2VpZ2h0X2RlY2F5lEsAdXUu",
+        "log_std_init": -2,
+        "ortho_init": false,
         "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>",
         "optimizer_kwargs": {
             "alpha": 0.99,
@@ -19,24 +21,24 @@
             "weight_decay": 0
         }
     },
-    "num_timesteps": 1000000,
-    "_total_timesteps": 1000000,
+    "num_timesteps": 2000000,
+    "_total_timesteps": 2000000,
     "_num_timesteps_at_start": 0,
     "seed": null,
     "action_noise": null,
-    "start_time": 1690632286413393997,
-    "learning_rate": 0.0007,
+    "start_time": 1690989364346012666,
+    "learning_rate": 0.00096,
     "tensorboard_log": null,
     "lr_schedule": {
         ":type:": "<class 'function'>",
-        ":serialized:": "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"
+        ":serialized:": "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"
     },
     "_last_obs": {
         ":type:": "<class 'collections.OrderedDict'>",
-        ":serialized:": "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",
-        "achieved_goal": "[[ 0.40298134 -0.02080084  0.5897815 ]\n [ 0.40298134 -0.02080084  0.5897815 ]\n [ 0.40298134 -0.02080084  0.5897815 ]\n [ 0.40298134 -0.02080084  0.5897815 ]]",
-        "desired_goal": "[[ 1.3680414  -1.0249933  -0.9140718 ]\n [ 1.3136592  -0.33240986  0.9436171 ]\n [-0.65505266  0.571432    1.1237466 ]\n [ 1.0585778  -1.1843046   0.90149033]]",
-        "observation": "[[ 4.0298134e-01 -2.0800842e-02  5.8978152e-01  1.3715383e-02\n   4.8564389e-04  1.4995327e-02]\n [ 4.0298134e-01 -2.0800842e-02  5.8978152e-01  1.3715383e-02\n   4.8564389e-04  1.4995327e-02]\n [ 4.0298134e-01 -2.0800842e-02  5.8978152e-01  1.3715383e-02\n   4.8564389e-04  1.4995327e-02]\n [ 4.0298134e-01 -2.0800842e-02  5.8978152e-01  1.3715383e-02\n   4.8564389e-04  1.4995327e-02]]"
+        ":serialized:": "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",
+        "achieved_goal": "[[ 0.39713338 -0.00535527  0.5659212 ]\n [ 0.39713338 -0.00535527  0.5659212 ]\n [ 0.39713338 -0.00535527  0.5659212 ]\n [ 0.39713338 -0.00535527  0.5659212 ]]",
+        "desired_goal": "[[ 0.28122342  0.7022284   0.20411971]\n [ 0.75519896 -0.43266648 -0.8312646 ]\n [-1.2622699  -1.6792365  -1.1135107 ]\n [ 0.73157567 -0.05730081  0.16099781]]",
+        "observation": "[[ 0.39713338 -0.00535527  0.5659212   0.03524084  0.00117638  0.03807412]\n [ 0.39713338 -0.00535527  0.5659212   0.03524084  0.00117638  0.03807412]\n [ 0.39713338 -0.00535527  0.5659212   0.03524084  0.00117638  0.03807412]\n [ 0.39713338 -0.00535527  0.5659212   0.03524084  0.00117638  0.03807412]]"
     },
     "_last_episode_starts": {
         ":type:": "<class 'numpy.ndarray'>",
@@ -44,30 +46,30 @@
     },
     "_last_original_obs": {
         ":type:": "<class 'collections.OrderedDict'>",
-        ":serialized:": "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",
+        ":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": "[[-0.0819015   0.04050631  0.0299799 ]\n [-0.11453149  0.07108197  0.19107324]\n [ 0.11134691 -0.09369774  0.1159567 ]\n [ 0.12286872 -0.10318077  0.13581638]]",
+        "desired_goal": "[[-0.00303262  0.11712053  0.00524051]\n [ 0.11989045 -0.13494492  0.14094292]\n [ 0.02034681 -0.06580988  0.08514864]\n [-0.0768382   0.0662178   0.15132089]]",
         "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,
+    "use_sde": true,
     "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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"
+        ":serialized:": "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"
     },
     "ep_success_buffer": {
         ":type:": "<class 'collections.deque'>",
         ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
     },
-    "_n_updates": 50000,
-    "n_steps": 5,
+    "_n_updates": 62500,
+    "n_steps": 8,
     "gamma": 0.99,
-    "gae_lambda": 1.0,
+    "gae_lambda": 0.9,
     "ent_coef": 0.0,
-    "vf_coef": 0.5,
+    "vf_coef": 0.4,
     "max_grad_norm": 0.5,
     "normalize_advantage": false,
     "observation_space": {

a2c-PandaReachDense-v2/policy.optimizer.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:2de2687af523b015ce17889bf3f71414084d3d365882bc4570d33fa4d68c4b6e
-size 44734
+oid sha256:1c4332dc6b9c3d6ea05e34356d917d30c88f895e79c29cfe46006b1ddfb44ef1
+size 45438

a2c-PandaReachDense-v2/policy.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:b122d2e6409bf7829904d36b9ac5561576d6f7d07e488c4d2367ffb37eff45a1
-size 46014
+oid sha256:3ed3a07f99ca824f0c723baa45c6e9a8aa06bafd718fea698908e411dc8e76ff
+size 46718

a2c-PandaReachDense-v2/system_info.txt

@@ -1,5 +1,5 @@
 - OS: Linux-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023
-- Python: 3.10.6
+- Python: 3.10.12
 - Stable-Baselines3: 1.8.0
 - PyTorch: 2.0.1+cu118
 - GPU Enabled: True

config.json

@@ -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 0x7dc7e60069e0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7dc7e6002900>"}, "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": 1690632286413393997, "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": "[[ 0.40298134 -0.02080084  0.5897815 ]\n [ 0.40298134 -0.02080084  0.5897815 ]\n [ 0.40298134 -0.02080084  0.5897815 ]\n [ 0.40298134 -0.02080084  0.5897815 ]]", "desired_goal": "[[ 1.3680414  -1.0249933  -0.9140718 ]\n [ 1.3136592  -0.33240986  0.9436171 ]\n [-0.65505266  0.571432    1.1237466 ]\n [ 1.0585778  -1.1843046   0.90149033]]", "observation": "[[ 4.0298134e-01 -2.0800842e-02  5.8978152e-01  1.3715383e-02\n   4.8564389e-04  1.4995327e-02]\n [ 4.0298134e-01 -2.0800842e-02  5.8978152e-01  1.3715383e-02\n   4.8564389e-04  1.4995327e-02]\n [ 4.0298134e-01 -2.0800842e-02  5.8978152e-01  1.3715383e-02\n   4.8564389e-04  1.4995327e-02]\n [ 4.0298134e-01 -2.0800842e-02  5.8978152e-01  1.3715383e-02\n   4.8564389e-04  1.4995327e-02]]"}, "_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": "[[-0.0819015   0.04050631  0.0299799 ]\n [-0.11453149  0.07108197  0.19107324]\n [ 0.11134691 -0.09369774  0.1159567 ]\n [ 0.12286872 -0.10318077  0.13581638]]", "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"}}
+{"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 0x7a695ed02e60>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7a695ed08c00>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gAWVowAAAAAAAAB9lCiMDGxvZ19zdGRfaW5pdJRK/v///4wKb3J0aG9faW5pdJSJjA9vcHRpbWl6ZXJfY2xhc3OUjBN0b3JjaC5vcHRpbS5ybXNwcm9wlIwHUk1TcHJvcJSTlIwQb3B0aW1pemVyX2t3YXJnc5R9lCiMBWFscGhhlEc/764UeuFHrowDZXBzlEc+5Pi1iONo8YwMd2VpZ2h0X2RlY2F5lEsAdXUu", "log_std_init": -2, "ortho_init": false, "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": 1690989364346012666, "learning_rate": 0.00096, "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": "[[ 0.39713338 -0.00535527  0.5659212 ]\n [ 0.39713338 -0.00535527  0.5659212 ]\n [ 0.39713338 -0.00535527  0.5659212 ]\n [ 0.39713338 -0.00535527  0.5659212 ]]", "desired_goal": "[[ 0.28122342  0.7022284   0.20411971]\n [ 0.75519896 -0.43266648 -0.8312646 ]\n [-1.2622699  -1.6792365  -1.1135107 ]\n [ 0.73157567 -0.05730081  0.16099781]]", "observation": "[[ 0.39713338 -0.00535527  0.5659212   0.03524084  0.00117638  0.03807412]\n [ 0.39713338 -0.00535527  0.5659212   0.03524084  0.00117638  0.03807412]\n [ 0.39713338 -0.00535527  0.5659212   0.03524084  0.00117638  0.03807412]\n [ 0.39713338 -0.00535527  0.5659212   0.03524084  0.00117638  0.03807412]]"}, "_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": "[[-0.00303262  0.11712053  0.00524051]\n [ 0.11989045 -0.13494492  0.14094292]\n [ 0.02034681 -0.06580988  0.08514864]\n [-0.0768382   0.0662178   0.15132089]]", "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": true, "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": 62500, "n_steps": 8, "gamma": 0.99, "gae_lambda": 0.9, "ent_coef": 0.0, "vf_coef": 0.4, "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"}}

results.json

@@ -1 +1 @@
-{"mean_reward": -4.034858308080584, "std_reward": 0.4775269546075044, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-07-29T13:02:10.878735"}
+{"mean_reward": -1.0501649817451835, "std_reward": 0.3174208517744026, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-08-02T16:53:28.897748"}

vec_normalize.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:834850b919ab36661c28e4e8cac09afab6ffc7f66373350531fa9e2fe7347a79
+oid sha256:ba80daf89fa043e147e8764f7538fa743c577082bbb999616b1b83c5cf3c3c16
 size 2387