🚀 tweaked back but still trained longer

README.md

@@ -10,7 +10,7 @@ model-index:
   results:
   - metrics:
     - type: mean_reward
-      value: -359.01 +/- 109.06
+      value: 257.95 +/- 18.69
       name: mean_reward
     task:
       type: reinforcement-learning

config.json

@@ -1 +1 @@
-{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gASVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__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 0x7faba0117c20>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7faba0117cb0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7faba0117d40>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7faba0117dd0>", "_build": "<function ActorCriticPolicy._build at 0x7faba0117e60>", "forward": "<function ActorCriticPolicy.forward at 0x7faba0117ef0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7faba0117f80>", "_predict": "<function ActorCriticPolicy._predict at 0x7faba011f050>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7faba011f0e0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7faba011f170>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7faba011f200>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7faba00ed600>"}, "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:": "gASVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": 4, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "num_timesteps": 114688, "_total_timesteps": 100000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1653172183.1410348, "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:": "gASVmAAAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSxCFlGgDjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYolDEAAAAAAAAAAAAAAAAAAAAACUdJRiLg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.1468799999999999, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 28, "n_steps": 1024, "gamma": 0.998, "gae_lambda": 0.97, "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-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022", "Python": "3.7.13", "Stable-Baselines3": "1.5.0", "PyTorch": "1.11.0+cu113", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}
+{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gASVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__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 0x7faba0117c20>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7faba0117cb0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7faba0117d40>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7faba0117dd0>", "_build": "<function ActorCriticPolicy._build at 0x7faba0117e60>", "forward": "<function ActorCriticPolicy.forward at 0x7faba0117ef0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7faba0117f80>", "_predict": "<function ActorCriticPolicy._predict at 0x7faba011f050>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7faba011f0e0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7faba011f170>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7faba011f200>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7faba00ed600>"}, "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:": "gASVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////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": 1653172968.0711062, "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:": "gASVmAAAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSxCFlGgDjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYolDEAAAAAAAAAAAAAAAAAAAAACUdJRiLg=="}, "_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:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 276, "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-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022", "Python": "3.7.13", "Stable-Baselines3": "1.5.0", "PyTorch": "1.11.0+cu113", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}

ppo-LunarLander-v2.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:44ebca280a742d03673cedd247f9afeacc7f8ed53cb465fdf68507a5c8094e38
-size 144021
+oid sha256:60d3a11aecc9ebe00149a7dd9b5e1f73532ac1d50138afa886ce435eb20afe48
+size 144198

ppo-LunarLander-v2/data

@@ -42,12 +42,12 @@
         "_np_random": null
     },
     "n_envs": 16,
-    "num_timesteps": 114688,
-    "_total_timesteps": 100000,
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
     "_num_timesteps_at_start": 0,
     "seed": null,
     "action_noise": null,
-    "start_time": 1653172183.1410348,
+    "start_time": 1653172968.0711062,
     "learning_rate": 0.0003,
     "tensorboard_log": null,
     "lr_schedule": {
@@ -56,7 +56,7 @@
     },
     "_last_obs": {
         ":type:": "<class 'numpy.ndarray'>",
-        ":serialized:": "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"
+        ":serialized:": "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"
     },
     "_last_episode_starts": {
         ":type:": "<class 'numpy.ndarray'>",
@@ -66,19 +66,19 @@
     "_episode_num": 0,
     "use_sde": false,
     "sde_sample_freq": -1,
-    "_current_progress_remaining": -0.1468799999999999,
+    "_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:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
     },
-    "_n_updates": 28,
+    "_n_updates": 276,
     "n_steps": 1024,
-    "gamma": 0.998,
-    "gae_lambda": 0.97,
+    "gamma": 0.999,
+    "gae_lambda": 0.98,
     "ent_coef": 0.01,
     "vf_coef": 0.5,
     "max_grad_norm": 0.5,

ppo-LunarLander-v2/policy.optimizer.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:5cf1dd361a257f57d246d1d36e82ada13a72ee3fdb9137272614cc3a4034d436
-size 84829
+oid sha256:3d23fa6ab40a7c1040174718c48ed4bd0ea4f6f1f649b9cf33b586473ad1f5d7
+size 84893

ppo-LunarLander-v2/policy.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:948c9965f6078a2e88ec29d950001541cec58677f5a443c9a53b2d04548be76d
+oid sha256:6355b431e5055d75b85275c524364117c52f8daee08c1e9182cd8d8b9206dbf7
 size 43201

replay.mp4

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:999e80d669ff062021d3152c9980317000de1dae3b105bfe65c72a6e25c54273
-size 132408
+oid sha256:af6b2a1a551810d29de3bca3c59761fc79f5202d9d440a6ce859c9cabdf8d329
+size 232007

results.json

@@ -1 +1 @@
-{"mean_reward": -359.01254940203364, "std_reward": 109.05632262556331, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-21T22:35:07.220638"}
+{"mean_reward": 257.9470281722487, "std_reward": 18.693215824711253, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-21T22:59:47.113788"}