hyperparameter tuning

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 251.23 +/- 25.08
+      value: 294.88 +/- 14.33
       name: mean_reward
       verified: false
 ---

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 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 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 ActorCriticPolicy.__init__ at 0x7a6985231990>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7a6985231a20>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7a6985231ab0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7a6985231b40>", "_build": "<function ActorCriticPolicy._build at 0x7a6985231bd0>", "forward": "<function ActorCriticPolicy.forward at 0x7a6985231c60>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7a6985231cf0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7a6985231d80>", "_predict": "<function ActorCriticPolicy._predict at 0x7a6985231e10>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7a6985231ea0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7a6985231f30>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7a6985231fc0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7a69851c5980>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1701929572964591939, "learning_rate": 0.0003, "tensorboard_log": null, "_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, "_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": 248, "observation_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "bounded_below": "[ True  True  True  True  True  True  True  True]", "bounded_above": "[ True  True  True  True  True  True  True  True]", "_shape": [8], "low": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]", "high": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]", "low_repr": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]", "high_repr": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]", "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.discrete.Discrete'>", ":serialized:": "gAWV1QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCloCmgOjApfbnBfcmFuZG9tlE51Yi4=", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "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, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Linux-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.1.0+cu118", "GPU Enabled": "True", "Numpy": "1.23.5", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}
+{"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 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 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 ActorCriticPolicy.__init__ at 0x7a6985231990>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7a6985231a20>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7a6985231ab0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7a6985231b40>", "_build": "<function ActorCriticPolicy._build at 0x7a6985231bd0>", "forward": "<function ActorCriticPolicy.forward at 0x7a6985231c60>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7a6985231cf0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7a6985231d80>", "_predict": "<function ActorCriticPolicy._predict at 0x7a6985231e10>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7a6985231ea0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7a6985231f30>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7a6985231fc0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7a69851c5980>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1507328, "_total_timesteps": 1500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1701932160007474252, "learning_rate": 0.0003, "tensorboard_log": null, "_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": -0.004885333333333408, "_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": 184, "observation_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "bounded_below": "[ True  True  True  True  True  True  True  True]", "bounded_above": "[ True  True  True  True  True  True  True  True]", "_shape": [8], "low": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]", "high": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]", "low_repr": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]", "high_repr": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]", "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.discrete.Discrete'>", ":serialized:": "gAWV1QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCloCmgOjApfbnBfcmFuZG9tlE51Yi4=", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "n_steps": 2048, "gamma": 0.999, "gae_lambda": 0.98, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 32, "n_epochs": 4, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Linux-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.1.0+cu118", "GPU Enabled": "True", "Numpy": "1.23.5", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}

karma-ppo-ll-next.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8dbf684807db217d9f51fd1359263be64869b9e908bdf9636cd6f7588830ee82
+size 147974

karma-ppo-ll-next/_stable_baselines3_version

@@ -0,0 +1 @@
+2.0.0a5

karma-ppo-ll-next/data

@@ -0,0 +1,99 @@
+{
+    "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 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 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 ActorCriticPolicy.__init__ at 0x7a6985231990>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7a6985231a20>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7a6985231ab0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7a6985231b40>",
+        "_build": "<function ActorCriticPolicy._build at 0x7a6985231bd0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7a6985231c60>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7a6985231cf0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7a6985231d80>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7a6985231e10>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7a6985231ea0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7a6985231f30>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7a6985231fc0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7a69851c5980>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1507328,
+    "_total_timesteps": 1500000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1701932160007474252,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "_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": -0.004885333333333408,
+    "_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": 184,
+    "observation_space": {
+        ":type:": "<class 'gymnasium.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "bounded_below": "[ True  True  True  True  True  True  True  True]",
+        "bounded_above": "[ True  True  True  True  True  True  True  True]",
+        "_shape": [
+            8
+        ],
+        "low": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]",
+        "high": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]",
+        "low_repr": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]",
+        "high_repr": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]",
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gymnasium.spaces.discrete.Discrete'>",
+        ":serialized:": "gAWV1QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCloCmgOjApfbnBfcmFuZG9tlE51Yi4=",
+        "n": "4",
+        "start": "0",
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": null
+    },
+    "n_envs": 16,
+    "n_steps": 2048,
+    "gamma": 0.999,
+    "gae_lambda": 0.98,
+    "ent_coef": 0.01,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "batch_size": 32,
+    "n_epochs": 4,
+    "clip_range": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "clip_range_vf": null,
+    "normalize_advantage": true,
+    "target_kl": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "gAWVxQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTAvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuEQwIEAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjEwL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB99lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz8zqSowVTJhhZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"
+    }
+}

karma-ppo-ll-next/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:17a005264645c2111e51e1d675ddfaaccd691ed38310f17d2be83073146c0737
+size 88362

karma-ppo-ll-next/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ac4b145cd52efff96597fd244f9da5f13961ab239ffceb1701df89f64655e495
+size 43762

karma-ppo-ll-next/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0c35cea3b2e60fb5e7e162d3592df775cd400e575a31c72f359fb9e654ab00c5
+size 864

karma-ppo-ll-next/system_info.txt

@@ -0,0 +1,9 @@
+- OS: Linux-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 UTC 2023
+- Python: 3.10.12
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.1.0+cu118
+- GPU Enabled: True
+- Numpy: 1.23.5
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

results.json

@@ -1 +1 @@
-{"mean_reward": 251.22520810000006, "std_reward": 25.079964541599065, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-12-07T06:46:42.276497"}
+{"mean_reward": 294.8781684, "std_reward": 14.330272088938136, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-12-07T08:22:57.378702"}