Reduce timesteps

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: -1.21 +/- 21.28
+      value: 167.01 +/- 24.17
       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 0x7f6e2ce1d5e0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f6e2ce1d670>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f6e2ce1d700>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f6e2ce1d790>", "_build": "<function ActorCriticPolicy._build at 0x7f6e2ce1d820>", "forward": "<function ActorCriticPolicy.forward at 0x7f6e2ce1d8b0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f6e2ce1d940>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f6e2ce1d9d0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f6e2ce1da60>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f6e2ce1daf0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f6e2ce1db80>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f6e2ce1dc10>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f6e2ce178d0>"}, "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": 1673976980616525458, "learning_rate": 0.0001, "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": 124, "n_steps": 2048, "gamma": 0.999, "gae_lambda": 0.99, "ent_coef": 0.0, "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-5.10.147+-x86_64-with-glibc2.27 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.16", "Stable-Baselines3": "1.7.0", "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 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 0x7f0b7cfdba60>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f0b7cfdbaf0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f0b7cfdbb80>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f0b7cfdbc10>", "_build": "<function ActorCriticPolicy._build at 0x7f0b7cfdbca0>", "forward": "<function ActorCriticPolicy.forward at 0x7f0b7cfdbd30>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f0b7cfdbdc0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f0b7cfdbe50>", "_predict": "<function ActorCriticPolicy._predict at 0x7f0b7cfdbee0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f0b7cfdbf70>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f0b7cfdf040>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f0b7cfdf0d0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f0b7cfd5b70>"}, "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": 1674007452553927438, "learning_rate": 0.0001, "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": 0.99, "ent_coef": 0.0, "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-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.10", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}

lunar_lander_v1.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:e6357dfd66013377ac993cde625d63a4461803f32a62f80f2c2c4b19c56cedf5
-size 147403
+oid sha256:37ed3c83b0844652df290c0e161ad60fef4cc64288f3f84d952e1ff5367939f4
+size 147423

lunar_lander_v1/data

@@ -4,20 +4,20 @@
         ":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 0x7f6e2ce1d5e0>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f6e2ce1d670>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f6e2ce1d700>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f6e2ce1d790>",
-        "_build": "<function ActorCriticPolicy._build at 0x7f6e2ce1d820>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7f6e2ce1d8b0>",
-        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f6e2ce1d940>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f6e2ce1d9d0>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7f6e2ce1da60>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f6e2ce1daf0>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f6e2ce1db80>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f6e2ce1dc10>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7f0b7cfdba60>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f0b7cfdbaf0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f0b7cfdbb80>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f0b7cfdbc10>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f0b7cfdbca0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f0b7cfdbd30>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f0b7cfdbdc0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f0b7cfdbe50>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f0b7cfdbee0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f0b7cfdbf70>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f0b7cfdf040>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f0b7cfdf0d0>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc_data object at 0x7f6e2ce178d0>"
+        "_abc_impl": "<_abc_data object at 0x7f0b7cfd5b70>"
     },
     "verbose": 1,
     "policy_kwargs": {},
@@ -48,7 +48,7 @@
     "_num_timesteps_at_start": 0,
     "seed": null,
     "action_noise": null,
-    "start_time": 1673976980616525458,
+    "start_time": 1674007452553927438,
     "learning_rate": 0.0001,
     "tensorboard_log": null,
     "lr_schedule": {
@@ -57,7 +57,7 @@
     },
     "_last_obs": {
         ":type:": "<class 'numpy.ndarray'>",
-        ":serialized:": "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"
+        ":serialized:": "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"
     },
     "_last_episode_starts": {
         ":type:": "<class 'numpy.ndarray'>",
@@ -70,14 +70,14 @@
     "_current_progress_remaining": -0.015808000000000044,
     "ep_info_buffer": {
         ":type:": "<class 'collections.deque'>",
-        ":serialized:": "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"
+        ":serialized:": "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"
     },
     "ep_success_buffer": {
         ":type:": "<class 'collections.deque'>",
         ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
     },
-    "_n_updates": 124,
-    "n_steps": 2048,
+    "_n_updates": 248,
+    "n_steps": 1024,
     "gamma": 0.999,
     "gae_lambda": 0.99,
     "ent_coef": 0.0,

lunar_lander_v1/policy.optimizer.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:d647c386adc9858b65115a9742060635cffeae3c874879254c1a8292b1e780f6
+oid sha256:84f0a2a96899cfbd5825400300ae3cd50262e36e2c26d33d9bd4c42b45c9262c
 size 87929

lunar_lander_v1/policy.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:3c754194b658bc49be20aa7e53346be30c3969e01a3cb81de78d5013ba701c4c
+oid sha256:d709a81bcca38c85fc9ca934a9c8869581dcee958c19971267bf7de2496e60cd
 size 43393

lunar_lander_v1/system_info.txt

@@ -1,7 +1,7 @@
-- OS: Linux-5.10.147+-x86_64-with-glibc2.27 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
-- Python: 3.8.16
+- OS: Linux-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.8.10
 - Stable-Baselines3: 1.7.0
-- PyTorch: 1.13.0+cu116
+- PyTorch: 1.13.1+cu116
 - GPU Enabled: True
 - Numpy: 1.21.6
 - Gym: 0.21.0

results.json

@@ -1 +1 @@
-{"mean_reward": -1.2068771690621816, "std_reward": 21.27612658450083, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-17T17:59:21.961784"}
+{"mean_reward": 167.01385739902474, "std_reward": 24.17164478803665, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-18T02:40:38.465093"}