creating my first RL model

-step-0-to-step-1000.meta.json

@@ -0,0 +1 @@
+{"step_id": 0, "content_type": "video/mp4", "encoder_version": {"backend": "ffmpeg", "version": "b'ffmpeg version 4.3 Copyright (c) 2000-2020 the FFmpeg developers\\nbuilt with gcc 7.3.0 (crosstool-NG 1.23.0.449-a04d0)\\nconfiguration: --prefix=/opt/conda/conda-bld/ffmpeg_1597178665428/_h_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placeh --cc=/opt/conda/conda-bld/ffmpeg_1597178665428/_build_env/bin/x86_64-conda_cos6-linux-gnu-cc --disable-doc --disable-openssl --enable-avresample --enable-gnutls --enable-hardcoded-tables --enable-libfreetype --enable-libopenh264 --enable-pic --enable-pthreads --enable-shared --disable-static --enable-version3 --enable-zlib --enable-libmp3lame\\nlibavutil      56. 51.100 / 56. 51.100\\nlibavcodec     58. 91.100 / 58. 91.100\\nlibavformat    58. 45.100 / 58. 45.100\\nlibavdevice    58. 10.100 / 58. 10.100\\nlibavfilter     7. 85.100 /  7. 85.100\\nlibavresample   4.  0.  0 /  4.  0.  0\\nlibswscale      5.  7.100 /  5.  7.100\\nlibswresample   3.  7.100 /  3.  7.100\\n'", "cmdline": ["ffmpeg", "-nostats", "-loglevel", "error", "-y", "-f", "rawvideo", "-s:v", "600x400", "-pix_fmt", "rgb24", "-framerate", "50", "-i", "-", "-vf", "scale=trunc(iw/2)*2:trunc(ih/2)*2", "-vcodec", "libx264", "-pix_fmt", "yuv420p", "-r", "50", "/home/user/data/Ahmad/HuggingFace/Lunar Lander/tmp/tmpy0ffdzoe/-step-0-to-step-1000.mp4"]}}

README.md

@@ -1,3 +1,37 @@
 ---
-{}
+library_name: stable-baselines3
+tags:
+- LunarLander-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: PPO
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: LunarLander-v2
+      type: LunarLander-v2
+    metrics:
+    - type: mean_reward
+      value: -275.93 +/- 125.05
+      name: mean_reward
+      verified: false
 ---
+
+# **PPO** Agent playing **LunarLander-v2**
+This is a trained model of a **PPO** agent playing **LunarLander-v2**
+using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
+
+## Usage (with Stable-baselines3)
+TODO: Add your code
+
+
+```python
+from stable_baselines3 import ...
+from huggingface_sb3 import load_from_hub
+
+...
+```

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 0x7f6e8c433ca0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f6e8c433d30>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f6e8c433dc0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f6e8c433e50>", "_build": "<function ActorCriticPolicy._build at 0x7f6e8c433ee0>", "forward": "<function ActorCriticPolicy.forward at 0x7f6e8c433f70>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f6e8c438040>", "_predict": "<function ActorCriticPolicy._predict at 0x7f6e8c4380d0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f6e8c438160>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f6e8c4381f0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f6e8c438280>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f6e8c4ae480>"}, "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": 10010624, "_total_timesteps": 10000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1671946454215817136, "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": -0.0010623999999999079, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 2444, "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": "3.8.16", "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 0x7f19383fbf70>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f1938400040>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f19384000d0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f1938400160>", "_build": "<function ActorCriticPolicy._build at 0x7f19384001f0>", "forward": "<function ActorCriticPolicy.forward at 0x7f1938400280>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f1938400310>", "_predict": "<function ActorCriticPolicy._predict at 0x7f19384003a0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f1938400430>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f19384004c0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f1938400550>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f19383fd270>"}, "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": 16384, "_total_timesteps": 10000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1671975143157103714, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAABoaYj24ZaI/iBiTPk+E5b6G5Ou96lV3vQAAAAAAAAAAZoJ8PEegrD/4s2A+8TLdvosjWDwwgsw9AAAAAAAAAACa8nQ9JSkkP/DwcT2nOYW/wxZQPkNZi7wAAAAAAAAAAAbLR76KDt0+Ay7ZvqPcj7/QIUo+Jy+JOwAAAAAAAAAAAEWuPInQED+uDM8+27mHv+hNab+TZvi+AAAAAAAAAAAaCme9tjo4P+SINb6L1o+/a11xPuZJyz0AAAAAAAAAAID1D75+3bw/ljL6vmMKF74OmgY+NR1euwAAAAAAAAAAMxHpvq6BET+ay0m/MRl9v3Irlz5FywU+AAAAAAAAAAC6ADS+vmqGP4KR3b6pwVS/cfe9PhMnwD4AAAAAAAAAAJ/1FL+jk1c/yu8lv5WUeb9I7Z+8oIVXvgAAAAAAAAAAGg2wvfb6vD81dRu/CW0CPtbfvz0o80Y+AAAAAAAAAADtJxY+PMZ8P1z3oT72Hiu/L9dKvq29Zb4AAAAAAAAAAJqGiDyGrpY/TBmbPVrWPr8Ufae8kvA2uwAAAAAAAAAAmkFhvF0ltz8cvjO/lNHbPv38kjw1TE0+AAAAAAAAAAAarw2/yYeSP8h8d7//g2+/W3SQP9sf2z4AAAAAAAAAANbJGT+WxyU/ntSfP5GGeb87T16/ag1KvgAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksQSwiGlIwBQ5R0lFKULg=="}, "_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": -0.6384000000000001, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 4, "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-5.4.0-135-generic-x86_64-with-glibc2.17 #152~18.04.2-Ubuntu SMP Tue Nov 29 08:23:49 UTC 2022", "Python": "3.8.13", "Stable-Baselines3": "1.6.2", "PyTorch": "1.12.1", "GPU Enabled": "True", "Numpy": "1.23.1", "Gym": "0.21.0"}}

ppo-LunarLander-v2.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:53f29ee5501e68452bc9068a5d0784034d2e40a08559f983e9214f51484f6cd3
+oid sha256:d520e150beadb38e463cb3765ca3bff6bb1e683c21c8c40c0c97564b3f7557f6
 size 147145

ppo-LunarLander-v2/data

@@ -4,19 +4,19 @@
         ":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 0x7f6e8c433ca0>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f6e8c433d30>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f6e8c433dc0>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f6e8c433e50>",
-        "_build": "<function ActorCriticPolicy._build at 0x7f6e8c433ee0>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7f6e8c433f70>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f6e8c438040>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7f6e8c4380d0>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f6e8c438160>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f6e8c4381f0>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f6e8c438280>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7f19383fbf70>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f1938400040>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f19384000d0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f1938400160>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f19384001f0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f1938400280>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f1938400310>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f19384003a0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f1938400430>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f19384004c0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f1938400550>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc_data object at 0x7f6e8c4ae480>"
+        "_abc_impl": "<_abc_data object at 0x7f19383fd270>"
     },
     "verbose": 1,
     "policy_kwargs": {},
@@ -42,21 +42,21 @@
         "_np_random": null
     },
     "n_envs": 16,
-    "num_timesteps": 10010624,
-    "_total_timesteps": 10000000,
+    "num_timesteps": 16384,
+    "_total_timesteps": 10000,
     "_num_timesteps_at_start": 0,
     "seed": null,
     "action_noise": null,
-    "start_time": 1671946454215817136,
+    "start_time": 1671975143157103714,
     "learning_rate": 0.0003,
     "tensorboard_log": null,
     "lr_schedule": {
         ":type:": "<class 'function'>",
-        ":serialized:": "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"
+        ":serialized:": "gAWV7QIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMXS9ob21lL3VzZXIvYW5hY29uZGEzL2VudnMvYWhtYWQvbGliL3B5dGhvbjMuOC9zaXRlLXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4BDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMXS9ob21lL3VzZXIvYW5hY29uZGEzL2VudnMvYWhtYWQvbGliL3B5dGhvbjMuOC9zaXRlLXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/M6kqMFUyYYWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="
     },
     "_last_obs": {
         ":type:": "<class 'numpy.ndarray'>",
-        ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAAJpOgDwUgKW6+JdcvN3h5zhKfAs6kA9QuAAAgD8AAIA/zW6Kvdr9wD8yEvi+Gt4uPsU/Mr1v3Ee+AAAAAAAAAAAA1xs9eyjeupYuLbzLfFg88qSiuynUPj0AAIA/AACAPxpYHD2Pm58/WBOJPe+uTL8JSA69q+TZPAAAAAAAAAAAM8sQO/YRIrwWTU++v+NLukoNozxLlBQ+AACAPwAAgD8mIY++AeeaP44fwr6KSA+/BR8nv1ZrI74AAAAAAAAAAJrzCby4zpi7LgwqvFp5kjyFEwM9Qk15vQAAgD8AAIA/gL8XPbkmmT+8fLM9qjtCvzAZU7ty5Jg8AAAAAAAAAADt5TG+70yXPwLX375U0Qe/ONDPvi12j74AAAAAAAAAAOiqib5CTx8/Bj2iPmMGKL+spc++S+Z+PgAAAAAAAAAAMwNDu+ytn7tkuD49/42FPBW2DL3bemM9AACAPwAAgD8A05o8ouW+P1onIz6dChs+cJOvvML4Eb0AAAAAAAAAAM1GrzxUXLg/tro1P4xe0T4+a528o8WzvQAAAAAAAAAA1sKAPoAMvT5WRM6+IfQSv+7ykz7Zfoe+AAAAAAAAAAAAUmu8Q5iIP5phTb1GZ3K/q7FevcZqOr0AAAAAAAAAAM1PpTyFHsy7mDhXvQWFAT1hRRe8CxoMPQAAgD8AAIA/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksQSwiGlIwBQ5R0lFKULg=="
+        ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAABoaYj24ZaI/iBiTPk+E5b6G5Ou96lV3vQAAAAAAAAAAZoJ8PEegrD/4s2A+8TLdvosjWDwwgsw9AAAAAAAAAACa8nQ9JSkkP/DwcT2nOYW/wxZQPkNZi7wAAAAAAAAAAAbLR76KDt0+Ay7ZvqPcj7/QIUo+Jy+JOwAAAAAAAAAAAEWuPInQED+uDM8+27mHv+hNab+TZvi+AAAAAAAAAAAaCme9tjo4P+SINb6L1o+/a11xPuZJyz0AAAAAAAAAAID1D75+3bw/ljL6vmMKF74OmgY+NR1euwAAAAAAAAAAMxHpvq6BET+ay0m/MRl9v3Irlz5FywU+AAAAAAAAAAC6ADS+vmqGP4KR3b6pwVS/cfe9PhMnwD4AAAAAAAAAAJ/1FL+jk1c/yu8lv5WUeb9I7Z+8oIVXvgAAAAAAAAAAGg2wvfb6vD81dRu/CW0CPtbfvz0o80Y+AAAAAAAAAADtJxY+PMZ8P1z3oT72Hiu/L9dKvq29Zb4AAAAAAAAAAJqGiDyGrpY/TBmbPVrWPr8Ufae8kvA2uwAAAAAAAAAAmkFhvF0ltz8cvjO/lNHbPv38kjw1TE0+AAAAAAAAAAAarw2/yYeSP8h8d7//g2+/W3SQP9sf2z4AAAAAAAAAANbJGT+WxyU/ntSfP5GGeb87T16/ag1KvgAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksQSwiGlIwBQ5R0lFKULg=="
     },
     "_last_episode_starts": {
         ":type:": "<class 'numpy.ndarray'>",
@@ -66,16 +66,16 @@
     "_episode_num": 0,
     "use_sde": false,
     "sde_sample_freq": -1,
-    "_current_progress_remaining": -0.0010623999999999079,
+    "_current_progress_remaining": -0.6384000000000001,
     "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": 2444,
+    "_n_updates": 4,
     "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:": "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"
     },
     "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:dabffbee4870d75014093371271a645acc6f98d8847970df8947e2744b05029a
-size 87929
+oid sha256:eb6907dca80415a0bfc3bffbad6b4ee232314e60e6d4abb3da7a4ff9f290649b
+size 87865

ppo-LunarLander-v2/policy.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:1f7b45c759a7402b4818ff42f8c742aefde0db8dbdf9fdd7133dd8d59b4440f0
+oid sha256:fce6c73ef503561f43338b6875503796e6de970a75d30683c94d1cc90a1a9100
 size 43201

ppo-LunarLander-v2/system_info.txt

@@ -1,7 +1,7 @@
-OS: Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022
-Python: 3.8.16
+OS: Linux-5.4.0-135-generic-x86_64-with-glibc2.17 #152~18.04.2-Ubuntu SMP Tue Nov 29 08:23:49 UTC 2022
+Python: 3.8.13
 Stable-Baselines3: 1.6.2
-PyTorch: 1.13.0+cu116
+PyTorch: 1.12.1
 GPU Enabled: True
-Numpy: 1.21.6
+Numpy: 1.23.1
 Gym: 0.21.0

results.json

@@ -1 +1 @@
-{"mean_reward": 292.83318049644464, "std_reward": 24.517218865709783, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-25T08:17:19.758751"}
+{"mean_reward": -275.9304438436986, "std_reward": 125.05194324852515, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-25T22:32:31.292424"}