My very second model

README.md

@@ -10,7 +10,7 @@ model-index:
   results:
   - metrics:
     - type: mean_reward
-      value: -298.76 +/- 94.36
+      value: -24.11 +/- 13.99
       name: mean_reward
     task:
       type: reinforcement-learning

config.json

@@ -1 +1 @@
-{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__module__": "stable_baselines3.common.policies", "__doc__": "\n    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\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 sde_net_arch: Network architecture for extracting features\n        when using gSDE. If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\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: Features extractor to use.\n    :param features_extractor_kwargs: Keyword arguments\n        to pass to the features extractor.\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 ActorCriticPolicy.__init__ at 0x7fb1dc2e0c10>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fb1dc2e0ca0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fb1dc2e0d30>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fb1dc2e0dc0>", "_build": "<function ActorCriticPolicy._build at 0x7fb1dc2e0e50>", "forward": "<function ActorCriticPolicy.forward at 0x7fb1dc2e0ee0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fb1dc2e0f70>", "_predict": "<function ActorCriticPolicy._predict at 0x7fb1dc2e3040>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fb1dc2e30d0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fb1dc2e3160>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fb1dc2e31f0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fb1dc2dd840>"}, "verbose": 1, "policy_kwargs": {}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [8], "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]", "high": "[inf inf inf inf inf inf inf inf]", "bounded_below": "[False False False False False False False False]", "bounded_above": "[False False False False False False False False]", "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.discrete.Discrete'>", ":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": 4, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 4, "num_timesteps": 53248, "_total_timesteps": 50000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1652177226.45135, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWV9QAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJaAAAAAAAAAANr89T30nIM/4qh/PjttD7/dZz0+DuiZPQAAAAAAAAAAGvE8Payaqzxtd+49uT9iv0iZN73gWIs8AAAAAAAAAAAAXES84te4PzJnmb4SJ6g+IK92PP0BdD0AAAAAAAAAAPN4Ar7v5EE+ntW+vuc+bL9Ji3o+4fEhPgAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksESwiGlIwBQ5R0lFKULg=="}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.0649599999999999, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 52, "n_steps": 1024, "gamma": 0.999, "gae_lambda": 0.98, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 4, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.13.0-40-generic-x86_64-with-glibc2.29 #45~20.04.1-Ubuntu SMP Mon Apr 4 09:38:31 UTC 2022", "Python": "3.8.10", "Stable-Baselines3": "1.5.0", "PyTorch": "1.11.0+cu102", "GPU Enabled": "False", "Numpy": "1.21.6", "Gym": "0.21.0"}}
+{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__module__": "stable_baselines3.common.policies", "__doc__": "\n    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\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 sde_net_arch: Network architecture for extracting features\n        when using gSDE. If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\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: Features extractor to use.\n    :param features_extractor_kwargs: Keyword arguments\n        to pass to the features extractor.\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 ActorCriticPolicy.__init__ at 0x7f84b4ff8c10>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f84b4ff8ca0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f84b4ff8d30>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f84b4ff8dc0>", "_build": "<function ActorCriticPolicy._build at 0x7f84b4ff8e50>", "forward": "<function ActorCriticPolicy.forward at 0x7f84b4ff8ee0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f84b4ff8f70>", "_predict": "<function ActorCriticPolicy._predict at 0x7f84b4ffc040>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f84b4ffc0d0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f84b4ffc160>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f84b4ffc1f0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f84b4ff5840>"}, "verbose": 1, "policy_kwargs": {}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "gAWVnwEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLCIWUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWIAAAAAAAAAAAAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/5RoCksIhZSMAUOUdJRSlIwEaGlnaJRoEiiWIAAAAAAAAAAAAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf5RoCksIhZRoFXSUUpSMDWJvdW5kZWRfYmVsb3eUaBIolggAAAAAAAAAAAAAAAAAAACUaAeMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLCIWUaBV0lFKUjA1ib3VuZGVkX2Fib3ZllGgSKJYIAAAAAAAAAAAAAAAAAAAAlGghSwiFlGgVdJRSlIwKX25wX3JhbmRvbZROdWIu", "dtype": "float32", "_shape": [8], "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]", "high": "[inf inf inf inf inf inf inf inf]", "bounded_below": "[False False False False False False False False]", "bounded_above": "[False False False False False False False False]", "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.discrete.Discrete'>", ":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": 4, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 8, "num_timesteps": 507904, "_total_timesteps": 500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1652178920.8024821, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVewAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYIAAAAAAAAAAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksIhZSMAUOUdJRSlC4="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.015808000000000044, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 124, "n_steps": 2048, "gamma": 0.9999, "gae_lambda": 0.97, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 256, "n_epochs": 4, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.13.0-40-generic-x86_64-with-glibc2.29 #45~20.04.1-Ubuntu SMP Mon Apr 4 09:38:31 UTC 2022", "Python": "3.8.10", "Stable-Baselines3": "1.5.0", "PyTorch": "1.11.0+cu102", "GPU Enabled": "False", "Numpy": "1.21.6", "Gym": "0.21.0"}}

replay.mp4

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:8a0bdc01f73d1407e935ae16b2c86b7ba363f9ebfd5377f2a1ca61e3778e972c
-size 191395
+oid sha256:792ca328efb982d8fdfe9308990160a4bb418a3990ddef9aa9c8befbe9a9901b
+size 267524

results.json

@@ -1 +1 @@
-{"mean_reward": -298.7584834892652, "std_reward": 94.35763359344253, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-10T12:26:15.429799"}
+{"mean_reward": -24.112017596559234, "std_reward": 13.994545998565814, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-10T13:05:10.323731"}

tocho1.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ee8f71d8672404467460024533897802608527725767d8525de4eda13ff0421e
+size 143500

tocho1/_stable_baselines3_version

@@ -0,0 +1 @@
+1.5.0

tocho1/data

@@ -0,0 +1,94 @@
+{
+    "policy_class": {
+        ":type:": "<class 'abc.ABCMeta'>",
+        ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
+        "__module__": "stable_baselines3.common.policies",
+        "__doc__": "\n    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\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 sde_net_arch: Network architecture for extracting features\n        when using gSDE. If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\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: Features extractor to use.\n    :param features_extractor_kwargs: Keyword arguments\n        to pass to the features extractor.\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 ActorCriticPolicy.__init__ at 0x7f84b4ff8c10>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f84b4ff8ca0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f84b4ff8d30>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f84b4ff8dc0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f84b4ff8e50>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f84b4ff8ee0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f84b4ff8f70>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f84b4ffc040>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f84b4ffc0d0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f84b4ffc160>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f84b4ffc1f0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f84b4ff5840>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "observation_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "_shape": [
+            8
+        ],
+        "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]",
+        "high": "[inf inf inf inf inf inf inf inf]",
+        "bounded_below": "[False False False False False False False False]",
+        "bounded_above": "[False False False False False False False False]",
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gym.spaces.discrete.Discrete'>",
+        ":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
+        "n": 4,
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": null
+    },
+    "n_envs": 8,
+    "num_timesteps": 507904,
+    "_total_timesteps": 500000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1652178920.8024821,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "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"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVewAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYIAAAAAAAAAAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksIhZSMAUOUdJRSlC4="
+    },
+    "_last_original_obs": null,
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": -0.015808000000000044,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 124,
+    "n_steps": 2048,
+    "gamma": 0.9999,
+    "gae_lambda": 0.97,
+    "ent_coef": 0.01,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "batch_size": 256,
+    "n_epochs": 4,
+    "clip_range": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "clip_range_vf": null,
+    "normalize_advantage": true,
+    "target_kl": null
+}

tocho1/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5e1bc64d8b6faf7cb9272fbba478155f528a8f79d7f1fb3b25086fc327c776c6
+size 84573

tocho1/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f3c8f3b4418fe80478fa6972ff6d185c5ffa613b0b25854f26f8f72a77f85dee
+size 43073

tocho1/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
+size 431

tocho1/system_info.txt

@@ -0,0 +1,7 @@
+OS: Linux-5.13.0-40-generic-x86_64-with-glibc2.29 #45~20.04.1-Ubuntu SMP Mon Apr 4 09:38:31 UTC 2022
+Python: 3.8.10
+Stable-Baselines3: 1.5.0
+PyTorch: 1.11.0+cu102
+GPU Enabled: False
+Numpy: 1.21.6
+Gym: 0.21.0