Upload best PPO LunarLander-v2 agent (tuned with Optuna).

PPO-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1429d56befa59e5f45114c1863d83391c81508a91b01b68b52f6c26a6ac1c717
+size 146665

PPO-LunarLander-v2/_stable_baselines3_version

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

PPO-LunarLander-v2/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 0x177d82d40>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x177d82dd0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x177d82e60>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x177d82ef0>",
+        "_build": "<function ActorCriticPolicy._build at 0x177d82f80>",
+        "forward": "<function ActorCriticPolicy.forward at 0x177d83010>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x177d830a0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x177d83130>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x177d831c0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x177d83250>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x177d832e0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x177d75f80>"
+    },
+    "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": 16,
+    "num_timesteps": 1572864,
+    "_total_timesteps": 1565439,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1656944194.513899,
+    "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:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="
+    },
+    "_last_original_obs": null,
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": -0.00474307845914157,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 384,
+    "n_steps": 1024,
+    "gamma": 0.994739418619968,
+    "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
+}

PPO-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:70ab8b663c3f033c835203f38d368c2457ed058ed320093c5b63b8fb63082839
+size 87481

PPO-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cc5624bc99c7ae0b5e955f7c82f67f2e4044991eb38b7880de2728b983f2862e
+size 43073

PPO-LunarLander-v2/pytorch_variables.pth

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

PPO-LunarLander-v2/system_info.txt

@@ -0,0 +1,7 @@
+OS: macOS-12.3.1-arm64-arm-64bit Darwin Kernel Version 21.4.0: Fri Mar 18 00:46:32 PDT 2022; root:xnu-8020.101.4~15/RELEASE_ARM64_T6000
+Python: 3.10.2
+Stable-Baselines3: 1.5.0
+PyTorch: 1.13.0.dev20220610
+GPU Enabled: False
+Numpy: 1.22.4
+Gym: 0.21.0

README.md

@@ -10,7 +10,7 @@ model-index:
   results:
   - metrics:
     - type: mean_reward
-      value: 233.45 +/- 10.41
+      value: 272.69 +/- 16.48
       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 0x162363760>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x1623637f0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x162363880>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x162363910>", "_build": "<function ActorCriticPolicy._build at 0x1623639a0>", "forward": "<function ActorCriticPolicy.forward at 0x162363a30>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x162363ac0>", "_predict": "<function ActorCriticPolicy._predict at 0x162363b50>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x162363be0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x162363c70>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x162363d00>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x162223340>"}, "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": 16, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1656876932.4346051, "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:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="}, "_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": 248, "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": "macOS-12.3.1-arm64-arm-64bit Darwin Kernel Version 21.4.0: Fri Mar 18 00:46:32 PDT 2022; root:xnu-8020.101.4~15/RELEASE_ARM64_T6000", "Python": "3.10.2", "Stable-Baselines3": "1.5.0", "PyTorch": "1.13.0.dev20220610", "GPU Enabled": "False", "Numpy": "1.22.4", "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 0x177d82d40>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x177d82dd0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x177d82e60>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x177d82ef0>", "_build": "<function ActorCriticPolicy._build at 0x177d82f80>", "forward": "<function ActorCriticPolicy.forward at 0x177d83010>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x177d830a0>", "_predict": "<function ActorCriticPolicy._predict at 0x177d83130>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x177d831c0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x177d83250>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x177d832e0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x177d75f80>"}, "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": 16, "num_timesteps": 1572864, "_total_timesteps": 1565439, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1656944194.513899, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAAHK6pL4RBBE/Po+WPt2fBL8OX0u+qkSFPgAAAAAAAAAATQskvUv6qD+gCOy+ZnYTv1injjpOqt+9AAAAAAAAAACzV9w9z+x6PgrhbL2bdJi+yqrcPMZJgz0AAAAAAAAAAJqivTwciHY+YmOxPCyYuL7swCk9w3yJvQAAAAAAAAAAYBedvrZB/D4t+6w+Bgj0vo56370e9J89AAAAAAAAAAAz/1Q8uB2Du6oPkrux/ZU837vDPB4FgL0AAIA/AACAP+YKPL3GZgg/cgZOu/rItr5piEO98sIMvQAAAAAAAAAA5nkVPY/eSbrC5Isy/9pqsAsxWDtkGQOzAACAPwAAgD9ALLu9KM7HPQL3lD6Hjoq+pKT4PMobxT0AAAAAAAAAAADYDjsc8q4/lPg/PTA75L4JpOu6QVSavAAAAAAAAAAAZt5jvvPUkj+eNOu+0XQgvxgfnr665Eq+AAAAAAAAAACGsx6+S+nuPh6kWD6EJsO+QHo1OxppAT4AAAAAAAAAAGaUkb10HcI+Pd6oPf4txb6W1C+9h0u8PQAAAAAAAAAAs1FNPYVky7s9k/+7+pN/PNZuLj3+KVm9AACAPwAAgD9ApRM+19EMPkOij76cCJ2+RainvOrvp70AAAAAAAAAAGZ5gzwUEKO6HpWQsqDSVbFjCQO6cHmDMwAAgD8AAIA/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksQSwiGlIwBQ5R0lFKULg=="}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.00474307845914157, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 384, "n_steps": 1024, "gamma": 0.994739418619968, "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": "macOS-12.3.1-arm64-arm-64bit Darwin Kernel Version 21.4.0: Fri Mar 18 00:46:32 PDT 2022; root:xnu-8020.101.4~15/RELEASE_ARM64_T6000", "Python": "3.10.2", "Stable-Baselines3": "1.5.0", "PyTorch": "1.13.0.dev20220610", "GPU Enabled": "False", "Numpy": "1.22.4", "Gym": "0.21.0"}}

replay.mp4

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:f06178dc6184044538d31baed1f00a10a493c9a415c200138858a4b789a2bd74
-size 409124
+oid sha256:294eea6d1c8eb702ab84f86c0e3b713cbf07ca2d0f18ff6fc514f51c22eedab2
+size 389224

results.json

@@ -1 +1 @@
-{"mean_reward": 233.4531705877119, "std_reward": 10.410489052945195, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-07-03T22:52:57.668828"}
+{"mean_reward": 272.6875254325315, "std_reward": 16.47881733074215, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-07-04T16:29:11.677392"}