3m training steps with linear learning rate scheduler

3m-linlr-ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9f827b54d53b4ff485955d1930a9454e0a1d7f2409f31f9bcc7ed7b897ffa843
+size 147358

3m-linlr-ppo-LunarLander-v2/_stable_baselines3_version

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

3m-linlr-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 0x7f8a805f5f70>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f8a805fa040>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f8a805fa0d0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f8a805fa160>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f8a805fa1f0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f8a805fa280>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f8a805fa310>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f8a805fa3a0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f8a805fa430>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f8a805fa4c0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f8a805fa550>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f8a805efd80>"
+    },
+    "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": 1,
+    "num_timesteps": 3014656,
+    "_total_timesteps": 3000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1671614346339791904,
+    "learning_rate": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": null,
+    "_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,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 736,
+    "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
+}

3m-linlr-ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8f7a89bbf9dad2562bc105d6db1f1f8ed7a2b4247fed2995bcf8c8925f263724
+size 88057

3m-linlr-ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6e820527b3d87d96e8f515afe694e9b62f63ef2b1595ef4924e302846ac051e2
+size 43201

3m-linlr-ppo-LunarLander-v2/pytorch_variables.pth

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

3m-linlr-ppo-LunarLander-v2/system_info.txt

@@ -0,0 +1,7 @@
+OS: Linux-5.15.79.1-microsoft-standard-WSL2-x86_64-with-glibc2.29 #1 SMP Wed Nov 23 01:01:46 UTC 2022
+Python: 3.8.10
+Stable-Baselines3: 1.6.2
+PyTorch: 1.13.1+cu117
+GPU Enabled: True
+Numpy: 1.23.5
+Gym: 0.21.0

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 289.11 +/- 20.47
+      value: 229.47 +/- 95.01
       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 0x7f9762304f70>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f9762309040>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f97623090d0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f9762309160>", "_build": "<function ActorCriticPolicy._build at 0x7f97623091f0>", "forward": "<function ActorCriticPolicy.forward at 0x7f9762309280>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f9762309310>", "_predict": "<function ActorCriticPolicy._predict at 0x7f97623093a0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f9762309430>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f97623094c0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f9762309550>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f97622fed80>"}, "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": 1, "num_timesteps": 15007744, "_total_timesteps": 15000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1671557815362637149, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": null, "_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": -0.0005162666666667093, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 3664, "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-5.15.79.1-microsoft-standard-WSL2-x86_64-with-glibc2.29 #1 SMP Wed Nov 23 01:01:46 UTC 2022", "Python": "3.8.10", "Stable-Baselines3": "1.6.2", "PyTorch": "1.13.1+cu117", "GPU Enabled": "True", "Numpy": "1.23.5", "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 0x7f8a805f5f70>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f8a805fa040>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f8a805fa0d0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f8a805fa160>", "_build": "<function ActorCriticPolicy._build at 0x7f8a805fa1f0>", "forward": "<function ActorCriticPolicy.forward at 0x7f8a805fa280>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f8a805fa310>", "_predict": "<function ActorCriticPolicy._predict at 0x7f8a805fa3a0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f8a805fa430>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f8a805fa4c0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f8a805fa550>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f8a805efd80>"}, "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": 1, "num_timesteps": 3014656, "_total_timesteps": 3000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1671614346339791904, "learning_rate": {":type:": "<class 'function'>", ":serialized:": "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"}, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": null, "_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, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 736, "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-5.15.79.1-microsoft-standard-WSL2-x86_64-with-glibc2.29 #1 SMP Wed Nov 23 01:01:46 UTC 2022", "Python": "3.8.10", "Stable-Baselines3": "1.6.2", "PyTorch": "1.13.1+cu117", "GPU Enabled": "True", "Numpy": "1.23.5", "Gym": "0.21.0"}}

results.json

@@ -1 +1 @@
-{"mean_reward": 289.11391960000003, "std_reward": 20.46743938955138, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-21T10:11:19.296302"}
+{"mean_reward": 229.46675189999996, "std_reward": 95.01154926356209, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-21T11:26:44.315420"}