First model

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 258.06 +/- 22.19
+      value: 284.12 +/- 26.41
       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 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 0x7efed7c615e0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7efed7c61670>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7efed7c61700>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7efed7c61790>", "_build": "<function ActorCriticPolicy._build at 0x7efed7c61820>", "forward": "<function ActorCriticPolicy.forward at 0x7efed7c618b0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7efed7c61940>", "_predict": "<function ActorCriticPolicy._predict at 0x7efed7c619d0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7efed7c61a60>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7efed7c61af0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7efed7c61b80>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7efed7c57e40>"}, "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": 1670507978944113563, "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": "Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022", "Python": "3.8.15", "Stable-Baselines3": "1.6.2", "PyTorch": "1.13.0+cu116", "GPU Enabled": "True", "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 0x7f5078373550>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f50783735e0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f5078373670>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f5078373700>", "_build": "<function ActorCriticPolicy._build at 0x7f5078373790>", "forward": "<function ActorCriticPolicy.forward at 0x7f5078373820>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f50783738b0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f5078373940>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f50783739d0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f5078373a60>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f5078373af0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f507836d870>"}, "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": 2015232, "_total_timesteps": 2000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1670510357188315171, "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": -0.007616000000000067, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 738, "n_steps": 1024, "gamma": 0.995, "gae_lambda": 0.97, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 6, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022", "Python": "3.8.15", "Stable-Baselines3": "1.6.2", "PyTorch": "1.13.0+cu116", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}

ppo-LunarLander-v2-new.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4a8d09dbc692c38e24dadf67eb8fbf991696fd4309a6b17931801aa4a0a59f18
+size 147218

ppo-LunarLander-v2-new/_stable_baselines3_version

@@ -0,0 +1 @@
+1.6.2

ppo-LunarLander-v2-new/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 0x7f5078373550>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f50783735e0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f5078373670>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f5078373700>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f5078373790>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f5078373820>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f50783738b0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f5078373940>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f50783739d0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f5078373a60>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f5078373af0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f507836d870>"
+    },
+    "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": 2015232,
+    "_total_timesteps": 2000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1670510357188315171,
+    "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": -0.007616000000000067,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 738,
+    "n_steps": 1024,
+    "gamma": 0.995,
+    "gae_lambda": 0.97,
+    "ent_coef": 0.01,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "batch_size": 64,
+    "n_epochs": 6,
+    "clip_range": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "clip_range_vf": null,
+    "normalize_advantage": true,
+    "target_kl": null
+}

ppo-LunarLander-v2-new/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:89028e97a2dc30bedd9beeae9f3cd801794bd71490052c874a2af9073d8ed892
+size 88057

ppo-LunarLander-v2-new/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4a93365b9b4c90f51119f885b6848b07b95bea5ac84232d919f62e0de5dfe6be
+size 43201

ppo-LunarLander-v2-new/pytorch_variables.pth

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

ppo-LunarLander-v2-new/system_info.txt

@@ -0,0 +1,7 @@
+OS: Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022
+Python: 3.8.15
+Stable-Baselines3: 1.6.2
+PyTorch: 1.13.0+cu116
+GPU Enabled: True
+Numpy: 1.21.6
+Gym: 0.21.0

results.json

@@ -1 +1 @@
-{"mean_reward": 258.0565214802109, "std_reward": 22.18801244554895, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-08T14:30:28.697534"}
+{"mean_reward": 284.121877524273, "std_reward": 26.41075329021846, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-08T15:17:56.219832"}