2

README.md

@@ -10,7 +10,7 @@ model-index:
   results:
   - metrics:
     - type: mean_reward
-      value: 201.47 +/- 16.84
+      value: 274.72 +/- 17.63
       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 0x7f1683bab560>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f1683bab5f0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f1683bab680>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f1683bab710>", "_build": "<function ActorCriticPolicy._build at 0x7f1683bab7a0>", "forward": "<function ActorCriticPolicy.forward at 0x7f1683bab830>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f1683bab8c0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f1683bab950>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f1683bab9e0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f1683baba70>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f1683babb00>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f1683b778a0>"}, "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": 507904, "_total_timesteps": 500000.0, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1654434659.3059483, "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": 124, "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"}}
+{"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 0x7f479d5e6b00>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f479d5e6b90>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f479d5e6c20>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f479d5e6cb0>", "_build": "<function ActorCriticPolicy._build at 0x7f479d5e6d40>", "forward": "<function ActorCriticPolicy.forward at 0x7f479d5e6dd0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f479d5e6e60>", "_predict": "<function ActorCriticPolicy._predict at 0x7f479d5e6ef0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f479d5e6f80>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f479d5ed050>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f479d5ed0e0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f479d633a80>"}, "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": 1507328, "_total_timesteps": 1500000.0, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1654437416.839396, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "gASVvwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwNX2J1aWx0aW5fdHlwZZSTlIwKTGFtYmRhVHlwZZSFlFKUKGgCjAhDb2RlVHlwZZSFlFKUKEsBSwBLAUsBSxNDBIgAUwCUToWUKYwBX5SFlIxIL3Vzci9sb2NhbC9saWIvcHl0aG9uMy43L2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lIwEZnVuY5RLgEMCAAGUjAN2YWyUhZQpdJRSlH2UKIwLX19wYWNrYWdlX1+UjBhzdGFibGVfYmFzZWxpbmVzMy5jb21tb26UjAhfX25hbWVfX5SMHnN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi51dGlsc5SMCF9fZmlsZV9flIxIL3Vzci9sb2NhbC9saWIvcHl0aG9uMy43L2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaCB9lH2UKGgXaA6MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgYjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz8zqSowVTJhhZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gASVjQIAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSxBLCIaUaAOMBWR0eXBllJOUjAJmNJSJiIeUUpQoSwOMATyUTk5OSv////9K/////0sAdJRiiUIAAgAAphkgvi8fNz9UMxe+/4fMvoqGpb05pCe9AAAAAAAAAABmaCe9w5FkuhpaJzueO/M38rBHO26W07kAAAAAAAAAAJroLb2pjmi8IkVMPhwUAL7048G9kizTvgAAgD8AAIA/gAWOPnzjAT+6dSu+sNGovtga7z1G5La9AAAAAAAAAADm2w+9IL2qP16lv75NG/m+Z/tjvEV8tb0AAAAAAAAAANqZ/L3XSgw+NlcWPnDrYb4/cpO8fahNOwAAAAAAAAAAzd71PDdDrj8EyLA+OmG8vlcF+TsIWQo+AAAAAAAAAABayPK9KawGOf0Sbjj8AZ20ba8EvN77lrcAAIA/AACAPzOtDz3D+VC6z4jAOEI0sTO6hoO60z7jtwAAgD8AAIA/rZsJvuz5zrtiStE50SH3N3/zQz1JeBO5AACAPwAAgD/6klY+FL4xP3Ov7L2/pOS+7/sUPrxlAr4AAAAAAAAAAM3QdD1F9Ig/X/MMPhrU/L4CO1s+LV+pPQAAAAAAAAAAmqmsPJxHG7yuk3i9hDXevGjBAj2Owms+AACAPwAAgD/mwMi9Pbc8uwZolD0RJiM9C26HPHnoB74AAIA/AACAP0Cvib0pFA+6AsWXOs3kIrXLhoe7kv+wuQAAgD8AAIA/GkwZvVEzWT7lAlq9L+uWvhXGSr1bgw89AAAAAAAAAACUdJRiLg=="}, "_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": -0.004885333333333408, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 368, "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 sha256:8f4612c0c59f29886076d1066ad80728c2f59cac251f48ecc42fde51a8114f7d
-size 144158
+oid sha256:f72ad0792273f69d2dbdf36de108d13f1f01f6d34fcab0c80c5875c3cf3a4947
+size 144143

ppo-LunarLander-v2/data

@@ -4,19 +4,19 @@
         ":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 0x7f1683bab560>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f1683bab5f0>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f1683bab680>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f1683bab710>",
-        "_build": "<function ActorCriticPolicy._build at 0x7f1683bab7a0>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7f1683bab830>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f1683bab8c0>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7f1683bab950>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f1683bab9e0>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f1683baba70>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f1683babb00>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7f479d5e6b00>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f479d5e6b90>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f479d5e6c20>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f479d5e6cb0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f479d5e6d40>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f479d5e6dd0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f479d5e6e60>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f479d5e6ef0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f479d5e6f80>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f479d5ed050>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f479d5ed0e0>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc_data object at 0x7f1683b778a0>"
+        "_abc_impl": "<_abc_data object at 0x7f479d633a80>"
     },
     "verbose": 1,
     "policy_kwargs": {},
@@ -42,12 +42,12 @@
         "_np_random": null
     },
     "n_envs": 16,
-    "num_timesteps": 507904,
-    "_total_timesteps": 500000.0,
+    "num_timesteps": 1507328,
+    "_total_timesteps": 1500000.0,
     "_num_timesteps_at_start": 0,
     "seed": null,
     "action_noise": null,
-    "start_time": 1654434659.3059483,
+    "start_time": 1654437416.839396,
     "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'>",
@@ -66,16 +66,16 @@
     "_episode_num": 0,
     "use_sde": false,
     "sde_sample_freq": -1,
-    "_current_progress_remaining": -0.015808000000000044,
+    "_current_progress_remaining": -0.004885333333333408,
     "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": 124,
+    "_n_updates": 368,
     "n_steps": 1024,
     "gamma": 0.999,
     "gae_lambda": 0.98,
@@ -86,7 +86,7 @@
     "n_epochs": 4,
     "clip_range": {
         ":type:": "<class 'function'>",
-        ":serialized:": "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"
+        ":serialized:": "gASVvwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwNX2J1aWx0aW5fdHlwZZSTlIwKTGFtYmRhVHlwZZSFlFKUKGgCjAhDb2RlVHlwZZSFlFKUKEsBSwBLAUsBSxNDBIgAUwCUToWUKYwBX5SFlIxIL3Vzci9sb2NhbC9saWIvcHl0aG9uMy43L2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lIwEZnVuY5RLgEMCAAGUjAN2YWyUhZQpdJRSlH2UKIwLX19wYWNrYWdlX1+UjBhzdGFibGVfYmFzZWxpbmVzMy5jb21tb26UjAhfX25hbWVfX5SMHnN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi51dGlsc5SMCF9fZmlsZV9flIxIL3Vzci9sb2NhbC9saWIvcHl0aG9uMy43L2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaCB9lH2UKGgXaA6MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgYjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz/TMzMzMzMzhZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"
     },
     "clip_range_vf": null,
     "normalize_advantage": true,

ppo-LunarLander-v2/policy.optimizer.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:d88853eec0f275511e9327fbca9958caa9a46493c62a00d57500647ef70f5973
-size 84829
+oid sha256:e8a0f4738c600eba4e8a201e5f20c751e64ddcc7de45293d64471ca0814b5422
+size 84893

ppo-LunarLander-v2/policy.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:567d4ad5db86c34b18c3e076ce895bb9b003b6473cd26bee7d8d591a66740f7f
+oid sha256:1ae37ceeeea7d9c7b6301a79e29ed60d786f265ce8753f7f5d3e32dd531e5c6f
 size 43201

replay.mp4

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:d9f617bbe6719bf4cc4baf42ce1eff124b47544700a552c2403b82f3c9e01cdf
-size 267092
+oid sha256:fd6a779f3a4dc0d5d11ffb4084eab3687c738a63f87be793f9baa3a1b68b2bc3
+size 191133

results.json

@@ -1 +1 @@
-{"mean_reward": 201.47189519541354, "std_reward": 16.837371196259966, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-06-05T13:46:19.708040"}
+{"mean_reward": 274.71748703727496, "std_reward": 17.629500076697706, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-06-05T14:22:21.162541"}