Upload PPO LunarLander-v2 trained agent

README.md

@@ -0,0 +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: -26.85 +/- 21.19
+      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

@@ -0,0 +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 0x7fd6704ea9d0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fd6704eaa60>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fd6704eaaf0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fd6704eab80>", "_build": "<function ActorCriticPolicy._build at 0x7fd6704eac10>", "forward": "<function ActorCriticPolicy.forward at 0x7fd6704eaca0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fd6704ead30>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fd6704eadc0>", "_predict": "<function ActorCriticPolicy._predict at 0x7fd6704eae50>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fd6704eaee0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fd6704eaf70>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fd6704ee040>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fd670566d80>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 200192, "_total_timesteps": 200000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1696215712230671915, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAAPn5b68Png9OhUyv2Rbt7wc3Au76ZCtvgAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.0009600000000000719, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 6256, "observation_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "gAWVcAIAAAAAAACMFGd5bW5hc2l1bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMDWJvdW5kZWRfYmVsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWCAAAAAAAAAABAQEBAQEBAZRoB4wCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksIhZSMAUOUdJRSlIwNYm91bmRlZF9hYm92ZZRoECiWCAAAAAAAAAABAQEBAQEBAZRoFEsIhZRoGHSUUpSMBl9zaGFwZZRLCIWUjANsb3eUaBAoliAAAAAAAAAAAAC0wgAAtMIAAKDAAACgwNsPScAAAKDAAAAAgAAAAICUaApLCIWUaBh0lFKUjARoaWdolGgQKJYgAAAAAAAAAAAAtEIAALRCAACgQAAAoEDbD0lAAACgQAAAgD8AAIA/lGgKSwiFlGgYdJRSlIwIbG93X3JlcHKUjFtbLTkwLiAgICAgICAgLTkwLiAgICAgICAgIC01LiAgICAgICAgIC01LiAgICAgICAgIC0zLjE0MTU5MjcgIC01LgogIC0wLiAgICAgICAgIC0wLiAgICAgICBdlIwJaGlnaF9yZXBylIxTWzkwLiAgICAgICAgOTAuICAgICAgICAgNS4gICAgICAgICA1LiAgICAgICAgIDMuMTQxNTkyNyAgNS4KICAxLiAgICAgICAgIDEuICAgICAgIF2UjApfbnBfcmFuZG9tlE51Yi4=", "dtype": "float32", "bounded_below": "[ True  True  True  True  True  True  True  True]", "bounded_above": "[ True  True  True  True  True  True  True  True]", "_shape": [8], "low": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]", "high": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]", "low_repr": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]", "high_repr": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]", "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.discrete.Discrete'>", ":serialized:": "gAWV1QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCloCmgOjApfbnBfcmFuZG9tlE51Yi4=", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 1, "n_steps": 256, "gamma": 0.999, "gae_lambda": 0.98, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 1024, "n_epochs": 4, "clip_range": {":type:": "<class 'function'>", ":serialized:": "gAWV6wIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMXC9ob21lL3N5c2NvbWFjaC8uY29uZGEvZW52cy9STC9saWIvcHl0aG9uMy44L3NpdGUtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lIwEZnVuY5RLhEMCAAGUjAN2YWyUhZQpdJRSlH2UKIwLX19wYWNrYWdlX1+UjBhzdGFibGVfYmFzZWxpbmVzMy5jb21tb26UjAhfX25hbWVfX5SMHnN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi51dGlsc5SMCF9fZmlsZV9flIxcL2hvbWUvc3lzY29tYWNoLy5jb25kYS9lbnZzL1JML2xpYi9weXRob24zLjgvc2l0ZS1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUdU5OaACMEF9tYWtlX2VtcHR5X2NlbGyUk5QpUpSFlHSUUpSMHGNsb3VkcGlja2xlLmNsb3VkcGlja2xlX2Zhc3SUjBJfZnVuY3Rpb25fc2V0c3RhdGWUk5RoH32UfZQoaBZoDYwMX19xdWFsbmFtZV9flIwZY29uc3RhbnRfZm4uPGxvY2Fscz4uZnVuY5SMD19fYW5ub3RhdGlvbnNfX5R9lIwOX19rd2RlZmF1bHRzX1+UTowMX19kZWZhdWx0c19flE6MCl9fbW9kdWxlX1+UaBeMB19fZG9jX1+UTowLX19jbG9zdXJlX1+UaACMCl9tYWtlX2NlbGyUk5RHP8mZmZmZmZqFlFKUhZSMF19jbG91ZHBpY2tsZV9zdWJtb2R1bGVzlF2UjAtfX2dsb2JhbHNfX5R9lHWGlIZSMC4="}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Linux-6.2.0-33-generic-x86_64-with-glibc2.17 # 33~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Thu Sep  7 10:33:52 UTC 2", "Python": "3.8.18", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.0.1+cu117", "GPU Enabled": "True", "Numpy": "1.24.4", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1"}}

ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f59f514a0fdf412f641185b02a807de21008472981c7483178207e79ad735951
+size 146180

ppo-LunarLander-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+2.0.0a5

ppo-LunarLander-v2/data

@@ -0,0 +1,99 @@
+{
+    "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 0x7fd6704ea9d0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fd6704eaa60>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fd6704eaaf0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fd6704eab80>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fd6704eac10>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fd6704eaca0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fd6704ead30>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fd6704eadc0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fd6704eae50>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fd6704eaee0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fd6704eaf70>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fd6704ee040>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7fd670566d80>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 200192,
+    "_total_timesteps": 200000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1696215712230671915,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAAPn5b68Png9OhUyv2Rbt7wc3Au76ZCtvgAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="
+    },
+    "_last_original_obs": null,
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": -0.0009600000000000719,
+    "_stats_window_size": 100,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 6256,
+    "observation_space": {
+        ":type:": "<class 'gymnasium.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "bounded_below": "[ True  True  True  True  True  True  True  True]",
+        "bounded_above": "[ True  True  True  True  True  True  True  True]",
+        "_shape": [
+            8
+        ],
+        "low": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]",
+        "high": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]",
+        "low_repr": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]",
+        "high_repr": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]",
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gymnasium.spaces.discrete.Discrete'>",
+        ":serialized:": "gAWV1QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCloCmgOjApfbnBfcmFuZG9tlE51Yi4=",
+        "n": "4",
+        "start": "0",
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": null
+    },
+    "n_envs": 1,
+    "n_steps": 256,
+    "gamma": 0.999,
+    "gae_lambda": 0.98,
+    "ent_coef": 0.01,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "batch_size": 1024,
+    "n_epochs": 4,
+    "clip_range": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "clip_range_vf": null,
+    "normalize_advantage": true,
+    "target_kl": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "gAWV6wIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMXC9ob21lL3N5c2NvbWFjaC8uY29uZGEvZW52cy9STC9saWIvcHl0aG9uMy44L3NpdGUtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lIwEZnVuY5RLhEMCAAGUjAN2YWyUhZQpdJRSlH2UKIwLX19wYWNrYWdlX1+UjBhzdGFibGVfYmFzZWxpbmVzMy5jb21tb26UjAhfX25hbWVfX5SMHnN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi51dGlsc5SMCF9fZmlsZV9flIxcL2hvbWUvc3lzY29tYWNoLy5jb25kYS9lbnZzL1JML2xpYi9weXRob24zLjgvc2l0ZS1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUdU5OaACMEF9tYWtlX2VtcHR5X2NlbGyUk5QpUpSFlHSUUpSMHGNsb3VkcGlja2xlLmNsb3VkcGlja2xlX2Zhc3SUjBJfZnVuY3Rpb25fc2V0c3RhdGWUk5RoH32UfZQoaBZoDYwMX19xdWFsbmFtZV9flIwZY29uc3RhbnRfZm4uPGxvY2Fscz4uZnVuY5SMD19fYW5ub3RhdGlvbnNfX5R9lIwOX19rd2RlZmF1bHRzX1+UTowMX19kZWZhdWx0c19flE6MCl9fbW9kdWxlX1+UaBeMB19fZG9jX1+UTowLX19jbG9zdXJlX1+UaACMCl9tYWtlX2NlbGyUk5RHPzOpKjBVMmGFlFKUhZSMF19jbG91ZHBpY2tsZV9zdWJtb2R1bGVzlF2UjAtfX2dsb2JhbHNfX5R9lHWGlIZSMC4="
+    }
+}

ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:62557e7f42981f9de1ed86e59ad33bbe0d88d69c83035c998f9ab31129e03567
+size 87929

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fe7b29694eaa8c0430d63d48e8d62c8027d2405756a552eb6e25023903a121c3
+size 43329

ppo-LunarLander-v2/pytorch_variables.pth

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

ppo-LunarLander-v2/system_info.txt

@@ -0,0 +1,8 @@
+- OS: Linux-6.2.0-33-generic-x86_64-with-glibc2.17 # 33~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Thu Sep  7 10:33:52 UTC 2
+- Python: 3.8.18
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.0.1+cu117
+- GPU Enabled: True
+- Numpy: 1.24.4
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1

results.json

@@ -0,0 +1 @@
+{"mean_reward": -26.851714699999995, "std_reward": 21.188723367898113, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-10-02T11:03:23.466494"}