Trained for 1M steps

README.md

@@ -16,7 +16,7 @@ model-index:
       type: PandaReachDense-v2
     metrics:
     - type: mean_reward
-      value: -2.93 +/- 1.10
+      value: -3.25 +/- 1.07
       name: mean_reward
       verified: false
 ---

a2c-PandaReachDense-v2.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:2ba2082506bd74a134a71caf072972d8a6cfa3a7693358b1874bcbe51cc0399d
+oid sha256:5c498274fe08056a0d876833f98aae615d83c5570f7dd2a200ff7881e1a40bae
 size 108023

a2c-PandaReachDense-v2/data

@@ -4,9 +4,9 @@
         ":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 0x7ff5508e4550>",
+        "__init__": "<function MultiInputActorCriticPolicy.__init__ at 0x7f2d7c581940>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc_data object at 0x7ff5508dcb70>"
+        "_abc_impl": "<_abc_data object at 0x7f2d7c579e70>"
     },
     "verbose": 1,
     "policy_kwargs": {

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 0x7ff5508e4550>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7ff5508dcb70>"}, "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}}, "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, "num_timesteps": 1000000, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1674567420040615945, "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": "[[ 0.39960432 -0.02292581  0.5490342 ]\n [ 0.39960432 -0.02292581  0.5490342 ]\n [ 0.39960432 -0.02292581  0.5490342 ]\n [ 0.39960432 -0.02292581  0.5490342 ]]", "desired_goal": "[[ 0.7042244   0.5047816   0.76149493]\n [-0.8367604  -1.3204697   0.07401162]\n [-0.04947131 -0.8655947   1.4148589 ]\n [ 0.53847003 -1.4363203   0.02871745]]", "observation": "[[ 0.39960432 -0.02292581  0.5490342   0.01240866 -0.00584859  0.01295443]\n [ 0.39960432 -0.02292581  0.5490342   0.01240866 -0.00584859  0.01295443]\n [ 0.39960432 -0.02292581  0.5490342   0.01240866 -0.00584859  0.01295443]\n [ 0.39960432 -0.02292581  0.5490342   0.01240866 -0.00584859  0.01295443]]"}, "_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": "[[ 0.08248947 -0.13198015  0.23206475]\n [ 0.11381304 -0.06248929  0.04299313]\n [-0.10325617 -0.12047931  0.00267667]\n [-0.12148955  0.10474129  0.1281383 ]]", "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, "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, "system_info": {"OS": "Linux-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.10", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "True", "Numpy": "1.21.6", "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 0x7f2d7c581940>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f2d7c579e70>"}, "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}}, "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, "num_timesteps": 1000000, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1674567420040615945, "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": "[[ 0.39960432 -0.02292581  0.5490342 ]\n [ 0.39960432 -0.02292581  0.5490342 ]\n [ 0.39960432 -0.02292581  0.5490342 ]\n [ 0.39960432 -0.02292581  0.5490342 ]]", "desired_goal": "[[ 0.7042244   0.5047816   0.76149493]\n [-0.8367604  -1.3204697   0.07401162]\n [-0.04947131 -0.8655947   1.4148589 ]\n [ 0.53847003 -1.4363203   0.02871745]]", "observation": "[[ 0.39960432 -0.02292581  0.5490342   0.01240866 -0.00584859  0.01295443]\n [ 0.39960432 -0.02292581  0.5490342   0.01240866 -0.00584859  0.01295443]\n [ 0.39960432 -0.02292581  0.5490342   0.01240866 -0.00584859  0.01295443]\n [ 0.39960432 -0.02292581  0.5490342   0.01240866 -0.00584859  0.01295443]]"}, "_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": "[[ 0.08248947 -0.13198015  0.23206475]\n [ 0.11381304 -0.06248929  0.04299313]\n [-0.10325617 -0.12047931  0.00267667]\n [-0.12148955  0.10474129  0.1281383 ]]", "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, "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, "system_info": {"OS": "Linux-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.10", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}

results.json

@@ -1 +1 @@
-{"mean_reward": -2.925861381739378, "std_reward": 1.0999669156338863, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-24T14:21:05.111311"}
+{"mean_reward": -3.250894347578287, "std_reward": 1.0692453933781845, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-24T14:33:29.717078"}

vec_normalize.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:a67a4da01604b5ef7cd9169e5c5d8dd7031cd9ef58028a1f67e13a4a380ed326
+oid sha256:94585bf28ac7bdca3f4d74044e38ad02f639cd073e9cc0df6ac7bec291a7c825
 size 3049