creating my first RL model
Browse files- README.md +1 -1
- config.json +1 -1
- ppo-LunarLander-v2.zip +2 -2
- ppo-LunarLander-v2/data +19 -19
- ppo-LunarLander-v2/policy.optimizer.pth +1 -1
- ppo-LunarLander-v2/policy.pth +1 -1
- results.json +1 -1
README.md
CHANGED
@@ -16,7 +16,7 @@ model-index:
|
|
16 |
type: LunarLander-v2
|
17 |
metrics:
|
18 |
- type: mean_reward
|
19 |
-
value:
|
20 |
name: mean_reward
|
21 |
verified: false
|
22 |
---
|
|
|
16 |
type: LunarLander-v2
|
17 |
metrics:
|
18 |
- type: mean_reward
|
19 |
+
value: 284.57 +/- 22.88
|
20 |
name: mean_reward
|
21 |
verified: false
|
22 |
---
|
config.json
CHANGED
@@ -1 +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 0x7fc64e352ca0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fc64e352d30>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fc64e352dc0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fc64e352e50>", "_build": "<function ActorCriticPolicy._build at 0x7fc64e352ee0>", "forward": "<function ActorCriticPolicy.forward at 0x7fc64e352f70>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fc64e357040>", "_predict": "<function ActorCriticPolicy._predict at 0x7fc64e3570d0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fc64e357160>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fc64e3571f0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fc64e357280>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fc64e34d840>"}, "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": 100007936, "_total_timesteps": 100000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1672213226022385047, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAAM29AD0pKBC6fhQSNVz4lTAGwuc7mNRltAAAgD8AAIA/ABhpOxSgprqUeZsynIF5MPBJ1bnEaBmzAACAPwAAgD8zi6e79vgoutWREj3KLigzj06Zu7W1YjMAAIA/AACAP2bi6Lyfz6G7SwCWPHuMkTwWkhE9aoZ2vQAAgD8AAIA/ALamPNKiz7vahh6+gb2NvQWfBLxMJLi+AAAAAAAAgD/NhNS7SGugusrRLrTGJtEu/BTROo3YnDMAAIA/AACAP9qRKj58tRg/GDS1vTyUUL8zUrc+qCZZvgAAAAAAAAAA5rsNvcOROrrFSnm6A36mtVtqZTu8SpA5AACAPwAAgD9mBho6rtGxupYYurc8U6Cy41H9uE6R1DYAAIA/AACAPyrpfr57ykY/NkgEvm9/Dr8HPw2/eMyYugAAAAAAAAAAzb/tvFqAuD9k3ze/TUWZPklqwzyctYQ9AAAAAAAAAABm8Cu8SO+RuiQWgzlN0WQ07z+GOTXVl7gAAIA/AACAPyA+P74S524/eg1ivhksOL/WKAi/AriAPAAAAAAAAAAAZnRaPIuIlj+g2Xg9kslTv3ViMT1mhOM9AAAAAAAAAAAzR4w7e/qcuiQSibXrzh2wduM3uuYJuTQAAIA/AACAP2aXtrxRiEY+kjT1O912Cr+Z80+9QKsfvQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////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": -7.935999999997279e-05, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVHRAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIS3UBLzNbckCUhpRSlIwBbJRLqIwBdJRHQOS4VEaya/h1fZQoaAZoCWgPQwh7v9GOW0NyQJSGlFKUaBVLj2gWR0DkuFV8uzyCdX2UKGgGaAloD0MIdvusMhPRckCUhpRSlGgVS69oFkdA5LhW1hkRSXV9lChoBmgJaA9DCKMCJ9uANnFAlIaUUpRoFUuRaBZHQOS4WLySV4Z1fZQoaAZoCWgPQwjlYaHWtOFzQJSGlFKUaBVLs2gWR0DkuFjmwqy4dX2UKGgGaAloD0MIkPeqlUnXckCUhpRSlGgVS6VoFkdA5LhZrZrYXnV9lChoBmgJaA9DCDy+vWsQcHBAlIaUUpRoFUuUaBZHQOS41sfigkF1fZQoaAZoCWgPQwhLlL2lHHhzQJSGlFKUaBVLrWgWR0DkuNeSGrS3dX2UKGgGaAloD0MIPdf34aAAc0CUhpRSlGgVS6ZoFkdA5LjYFDWsinV9lChoBmgJaA9DCKvN/6uOM3FAlIaUUpRoFUuMaBZHQOS42Wgi/wl1fZQoaAZoCWgPQwjjpDDvcclzQJSGlFKUaBVLpGgWR0DkuNoE0SAZdX2UKGgGaAloD0MIVfZdETwKckCUhpRSlGgVS5VoFkdA5LjaIMz/InV9lChoBmgJaA9DCDOLUGzFWnFAlIaUUpRoFUunaBZHQOS42zylN111fZQoaAZoCWgPQwgCY30Dk+pwQJSGlFKUaBVLjGgWR0DkuN1+0gKXdX2UKGgGaAloD0MIshGI1zXBc0CUhpRSlGgVS61oFkdA5LjdniWE9XV9lChoBmgJaA9DCD2dK0oJp3JAlIaUUpRoFUu/aBZHQOS43b5qM3t1fZQoaAZoCWgPQwgVyVcCadFyQJSGlFKUaBVLsGgWR0DkuN4oegctdX2UKGgGaAloD0MIVHB4QQQPcUCUhpRSlGgVS5toFkdA5LjgBbnoxHV9lChoBmgJaA9DCKj+QSTDxHJAlIaUUpRoFUu4aBZHQOS44AHPeHl1fZQoaAZoCWgPQwhYcD/gwRdxQJSGlFKUaBVLlWgWR0DkuOEse4kNdX2UKGgGaAloD0MI1QloIuxKckCUhpRSlGgVS6hoFkdA5LjjzVc2SHV9lChoBmgJaA9DCOhmf6AcAHNAlIaUUpRoFUuxaBZHQOS4485ZKWd1fZQoaAZoCWgPQwjqI/CHn65zQJSGlFKUaBVLsWgWR0DkuOW4iHIqdX2UKGgGaAloD0MItvXTf5YUc0CUhpRSlGgVS7VoFkdA5Ljm4Uvf0nV9lChoBmgJaA9DCH0G1JuR6XNAlIaUUpRoFUu1aBZHQOS4528K5TZ1fZQoaAZoCWgPQwhX6INl7G1xQJSGlFKUaBVLoWgWR0DkuOevWYnfdX2UKGgGaAloD0MIHViOkAHCcUCUhpRSlGgVS6NoFkdA5Ljn9ycTanV9lChoBmgJaA9DCNds5SU/gHRAlIaUUpRoFUuxaBZHQOS46GOwPiF1fZQoaAZoCWgPQwhiuhCr//FwQJSGlFKUaBVLqmgWR0DkuOm2uPmxdX2UKGgGaAloD0MI10//WbOucECUhpRSlGgVS5poFkdA5LjqoS13MnV9lChoBmgJaA9DCJNuS+QCLHNAlIaUUpRoFUuoaBZHQOS47CNQ0oB1fZQoaAZoCWgPQwjMJVXbTfZzQJSGlFKUaBVLrGgWR0DkuOzsu3+ddX2UKGgGaAloD0MI4zPZP8/sckCUhpRSlGgVS7NoFkdA5Ljs/T1CgXV9lChoBmgJaA9DCDiDv1+MtXJAlIaUUpRoFUuzaBZHQOS474H9m6J1fZQoaAZoCWgPQwhTCU/o9WFxQJSGlFKUaBVLq2gWR0DkuPAIMSbpdX2UKGgGaAloD0MILA/SU2Q0dECUhpRSlGgVS8VoFkdA5LjxDn/1hHV9lChoBmgJaA9DCDGyZI6lSHFAlIaUUpRoFUucaBZHQOS48VLzwtt1fZQoaAZoCWgPQwjjT1Q2bEtyQJSGlFKUaBVLnmgWR0DkuPF86V+rdX2UKGgGaAloD0MIxt/2BMnzckCUhpRSlGgVS61oFkdA5Lj0bEYO2HV9lChoBmgJaA9DCMe7I2N1k3FAlIaUUpRoFUuAaBZHQOS49JVjqfR1fZQoaAZoCWgPQwgz3lZ6bVRxQJSGlFKUaBVLpGgWR0DkuPTKyv9tdX2UKGgGaAloD0MIQrCqXn4wcECUhpRSlGgVS5poFkdA5Lj0+yquKXV9lChoBmgJaA9DCHdlFwzuU3NAlIaUUpRoFUugaBZHQOS49TB42TB1fZQoaAZoCWgPQwiLU62F2Qx0QJSGlFKUaBVLp2gWR0DkuPV+85CGdX2UKGgGaAloD0MIlnZqLrcLcUCUhpRSlGgVS55oFkdA5Lj1rgOz6nV9lChoBmgJaA9DCK/qrBYY7nFAlIaUUpRoFUuGaBZHQOS49f0XgtR1fZQoaAZoCWgPQwg/VvDb0IxyQJSGlFKUaBVLg2gWR0DkuPeCQtBfdX2UKGgGaAloD0MI0VrR5jisc0CUhpRSlGgVS6VoFkdA5Lj5geaKDXV9lChoBmgJaA9DCADK372jZG9AlIaUUpRoFUumaBZHQOS4+mHWSU11fZQoaAZoCWgPQwitTPil/klxQJSGlFKUaBVLoGgWR0DkuPx1HvtudX2UKGgGaAloD0MIixcLQ6TYcECUhpRSlGgVS5VoFkdA5Lj9U4aP0nV9lChoBmgJaA9DCPsGJjeK1XNAlIaUUpRoFUu7aBZHQOS4/08gZCR1fZQoaAZoCWgPQwhI/fUKS8hyQJSGlFKUaBVLumgWR0DkuQBKZDzAdX2UKGgGaAloD0MId700RUACc0CUhpRSlGgVS7VoFkdA5LkAS8BdU3V9lChoBmgJaA9DCO0RaoZUw01AlIaUUpRoFUtoaBZHQOS5AGuV5bB1fZQoaAZoCWgPQwjy6bEtQwZxQJSGlFKUaBVLi2gWR0DkuQBq1PWQdX2UKGgGaAloD0MIDcfzGdCfckCUhpRSlGgVS55oFkdA5LkBUpd8iXV9lChoBmgJaA9DCOVC5V8Ly3JAlIaUUpRoFUuOaBZHQOS5AYpe/pN1fZQoaAZoCWgPQwjf/IaJBnNxQJSGlFKUaBVLoWgWR0DkuQGv8qFzdX2UKGgGaAloD0MIZf88DZgYc0CUhpRSlGgVS5toFkdA5LkByUC7snV9lChoBmgJaA9DCNZVgVqMJ3FAlIaUUpRoFUuqaBZHQOS5An8n/kx1fZQoaAZoCWgPQwi7D0Bq02txQJSGlFKUaBVLomgWR0DkuQLD/lySdX2UKGgGaAloD0MIH/MBgc5NcUCUhpRSlGgVS6ZoFkdA5LkC4hMaj3V9lChoBmgJaA9DCFH4bB2cznJAlIaUUpRoFUuvaBZHQOS5CAHTqjd1fZQoaAZoCWgPQwiw6NZrOp9zQJSGlFKUaBVLqWgWR0DkuQhxG2CvdX2UKGgGaAloD0MIgZiEC/nfcUCUhpRSlGgVS59oFkdA5LkJuC5Et3V9lChoBmgJaA9DCKGgFK1c2G9AlIaUUpRoFUufaBZHQOS5CpA0Kqp1fZQoaAZoCWgPQwiX/iWpzFxyQJSGlFKUaBVLj2gWR0DkuQwdXDFZdX2UKGgGaAloD0MIk+UklP6eckCUhpRSlGgVS5FoFkdA5LkMSRr8BXV9lChoBmgJaA9DCEg0gSLWX3JAlIaUUpRoFUuNaBZHQOS5DbQu27Z1fZQoaAZoCWgPQwi9b3ztGQZzQJSGlFKUaBVLomgWR0DkuQ3pfx+bdX2UKGgGaAloD0MI1VsDWyXFckCUhpRSlGgVS7JoFkdA5LkPRYJVsHV9lChoBmgJaA9DCAh2/BcIrXJAlIaUUpRoFUupaBZHQOS5D41k1/F1fZQoaAZoCWgPQwhqUDQPYClyQJSGlFKUaBVLo2gWR0DkuRB9d/rjdX2UKGgGaAloD0MIYvVHGIZucECUhpRSlGgVS6FoFkdA5LkQxZEDyXV9lChoBmgJaA9DCB050hmYynNAlIaUUpRoFUu2aBZHQOS5EPrv9cd1fZQoaAZoCWgPQwjT2cngqH50QJSGlFKUaBVLwWgWR0DkuRIQnx8VdX2UKGgGaAloD0MIbD1DOGY0c0CUhpRSlGgVS7NoFkdA5LkSKDCgsnV9lChoBmgJaA9DCHiAJy3cxnBAlIaUUpRoFUuaaBZHQOS5FfPiT+x1fZQoaAZoCWgPQwgVrHE2nbpvQJSGlFKUaBVLlGgWR0DkuRaoUi6hdX2UKGgGaAloD0MIY0fjUH+pc0CUhpRSlGgVS7xoFkdA5LkY0hV2inV9lChoBmgJaA9DCAHbwYj9GXJAlIaUUpRoFUuoaBZHQOS5GZvNu+B1fZQoaAZoCWgPQwg90uC2No9xQJSGlFKUaBVLqmgWR0DkuRvXiBGydX2UKGgGaAloD0MI/0C5bZ+3ckCUhpRSlGgVS7VoFkdA5LkcxwqAjXV9lChoBmgJaA9DCEFHq1oSWnNAlIaUUpRoFUulaBZHQOS5HPpOerd1fZQoaAZoCWgPQwh97C5Q0rxwQJSGlFKUaBVLl2gWR0DkuR3ES/TLdX2UKGgGaAloD0MIegCL/DqBckCUhpRSlGgVS7NoFkdA5Lkeliay8nV9lChoBmgJaA9DCPd0dceiS3NAlIaUUpRoFUulaBZHQOS5HsXgtOF1fZQoaAZoCWgPQwi7tUyGYwNzQJSGlFKUaBVLnWgWR0DkuR/BeokzdX2UKGgGaAloD0MIgEQTKKLEcUCUhpRSlGgVS6RoFkdA5LkgLSuyNXV9lChoBmgJaA9DCP334LULtHJAlIaUUpRoFUuzaBZHQOS5IUg0TDh1fZQoaAZoCWgPQwggJXZtL/dxQJSGlFKUaBVLo2gWR0DkuSGAy2x6dX2UKGgGaAloD0MI+fcZF47kckCUhpRSlGgVS6VoFkdA5LkhwhwEQ3V9lChoBmgJaA9DCDAPmfLhVXFAlIaUUpRoFUuMaBZHQOS5JB0GNaR1fZQoaAZoCWgPQwgtJctJ6BtxQJSGlFKUaBVLsGgWR0DkuSbnNgSfdX2UKGgGaAloD0MIJ9nqckrmcUCUhpRSlGgVS6VoFkdA5Lko21D0DnV9lChoBmgJaA9DCN0KYTXW2HNAlIaUUpRoFUusaBZHQOS5Km9OARV1fZQoaAZoCWgPQwgUX+0ojmRyQJSGlFKUaBVLnGgWR0DkuSsez2OAdX2UKGgGaAloD0MItHbbhWbBckCUhpRSlGgVS69oFkdA5LkuUhmoSHV9lChoBmgJaA9DCHGt9rDXG3RAlIaUUpRoFUu+aBZHQOS5L7guRLd1ZS4="}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 24416, "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"}}
|
|
|
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 0x7f876fc53ca0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f876fc53d30>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f876fc53dc0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f876fc53e50>", "_build": "<function ActorCriticPolicy._build at 0x7f876fc53ee0>", "forward": "<function ActorCriticPolicy.forward at 0x7f876fc53f70>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f876fc57040>", "_predict": "<function ActorCriticPolicy._predict at 0x7f876fc570d0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f876fc57160>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f876fc571f0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f876fc57280>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f876fc4f840>"}, "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": 1000013824, "_total_timesteps": 1000000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1672286850869062241, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAAGab47yD5je8VfV+PqGHjj2iZnC8uBXIvAAAgD8AAIA/TTHBvfVHYj+2BTK+b0aBv8p1nr6d54s9AAAAAAAAAAAAwD+84dCwun/dJLicmRuzFl7jOTu5PDcAAIA/AACAP5rHBrzh+vK6ssiTO6TskTw0egs89nJ8vQAAgD8AAIA/cIh7vrMTQT8UcK68itYxv5pSH78XWQE+AAAAAAAAAADNpM67g0ghvP5L8b1c3K+9IaSsOz1RHj8AAIA/AACAP2Y7/L1zCEo/BaIkvgW9bL8xC7O+bld3PQAAAAAAAAAAACAuuwPOLbxU66c8l3cTPVaik71bg+w9AACAPwAAgD8Aolu9O7y9P2Ietr4vNs88N6ZPPc5rPT0AAAAAAAAAAJrzcD3BkLo/eLPWPkAuM7pQReA8hnkqPgAAAAAAAAAAM5TKPWD4oz4/+R2+7Hs7v87BJT7wxXu+AAAAAAAAAACzFno9XbkKPtcQqr7SLAu/CByFvLcphL4AAAAAAAAAALOuCD2ZbA8/A3i6vDQScL/Z9Zg9AuvGvQAAAAAAAAAAs8OtPRxFFT1WmdO+bmTFvnDqLL6DXNa+AAAAAAAAAADzsn6+A4A+P9twiD09ulO/gMYJv2UutT4AAAAAAAAAADNTIryfNIi7mrsWPNASTjzuGNI8bGUzvQAAgD8AAIA/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////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": -1.3824000000051129e-05, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 244144, "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
CHANGED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
-
size
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:b01b4d56465edba3a4ba67fc004e624c9d7b183435c87723d4d1f09d616bf057
|
3 |
+
size 147168
|
ppo-LunarLander-v2/data
CHANGED
@@ -4,19 +4,19 @@
|
|
4 |
":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
|
5 |
"__module__": "stable_baselines3.common.policies",
|
6 |
"__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 ",
|
7 |
-
"__init__": "<function ActorCriticPolicy.__init__ at
|
8 |
-
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at
|
9 |
-
"reset_noise": "<function ActorCriticPolicy.reset_noise at
|
10 |
-
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at
|
11 |
-
"_build": "<function ActorCriticPolicy._build at
|
12 |
-
"forward": "<function ActorCriticPolicy.forward at
|
13 |
-
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at
|
14 |
-
"_predict": "<function ActorCriticPolicy._predict at
|
15 |
-
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at
|
16 |
-
"get_distribution": "<function ActorCriticPolicy.get_distribution at
|
17 |
-
"predict_values": "<function ActorCriticPolicy.predict_values at
|
18 |
"__abstractmethods__": "frozenset()",
|
19 |
-
"_abc_impl": "<_abc_data object at
|
20 |
},
|
21 |
"verbose": 1,
|
22 |
"policy_kwargs": {},
|
@@ -42,12 +42,12 @@
|
|
42 |
"_np_random": null
|
43 |
},
|
44 |
"n_envs": 16,
|
45 |
-
"num_timesteps":
|
46 |
-
"_total_timesteps":
|
47 |
"_num_timesteps_at_start": 0,
|
48 |
"seed": null,
|
49 |
"action_noise": null,
|
50 |
-
"start_time":
|
51 |
"learning_rate": 0.0003,
|
52 |
"tensorboard_log": null,
|
53 |
"lr_schedule": {
|
@@ -56,7 +56,7 @@
|
|
56 |
},
|
57 |
"_last_obs": {
|
58 |
":type:": "<class 'numpy.ndarray'>",
|
59 |
-
":serialized:": "
|
60 |
},
|
61 |
"_last_episode_starts": {
|
62 |
":type:": "<class 'numpy.ndarray'>",
|
@@ -66,16 +66,16 @@
|
|
66 |
"_episode_num": 0,
|
67 |
"use_sde": false,
|
68 |
"sde_sample_freq": -1,
|
69 |
-
"_current_progress_remaining": -
|
70 |
"ep_info_buffer": {
|
71 |
":type:": "<class 'collections.deque'>",
|
72 |
-
":serialized:": "
|
73 |
},
|
74 |
"ep_success_buffer": {
|
75 |
":type:": "<class 'collections.deque'>",
|
76 |
":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
|
77 |
},
|
78 |
-
"_n_updates":
|
79 |
"n_steps": 1024,
|
80 |
"gamma": 0.999,
|
81 |
"gae_lambda": 0.98,
|
|
|
4 |
":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
|
5 |
"__module__": "stable_baselines3.common.policies",
|
6 |
"__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 ",
|
7 |
+
"__init__": "<function ActorCriticPolicy.__init__ at 0x7f876fc53ca0>",
|
8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f876fc53d30>",
|
9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f876fc53dc0>",
|
10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f876fc53e50>",
|
11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7f876fc53ee0>",
|
12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7f876fc53f70>",
|
13 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f876fc57040>",
|
14 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7f876fc570d0>",
|
15 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f876fc57160>",
|
16 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f876fc571f0>",
|
17 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7f876fc57280>",
|
18 |
"__abstractmethods__": "frozenset()",
|
19 |
+
"_abc_impl": "<_abc_data object at 0x7f876fc4f840>"
|
20 |
},
|
21 |
"verbose": 1,
|
22 |
"policy_kwargs": {},
|
|
|
42 |
"_np_random": null
|
43 |
},
|
44 |
"n_envs": 16,
|
45 |
+
"num_timesteps": 1000013824,
|
46 |
+
"_total_timesteps": 1000000000,
|
47 |
"_num_timesteps_at_start": 0,
|
48 |
"seed": null,
|
49 |
"action_noise": null,
|
50 |
+
"start_time": 1672286850869062241,
|
51 |
"learning_rate": 0.0003,
|
52 |
"tensorboard_log": null,
|
53 |
"lr_schedule": {
|
|
|
56 |
},
|
57 |
"_last_obs": {
|
58 |
":type:": "<class 'numpy.ndarray'>",
|
59 |
+
":serialized:": "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"
|
60 |
},
|
61 |
"_last_episode_starts": {
|
62 |
":type:": "<class 'numpy.ndarray'>",
|
|
|
66 |
"_episode_num": 0,
|
67 |
"use_sde": false,
|
68 |
"sde_sample_freq": -1,
|
69 |
+
"_current_progress_remaining": -1.3824000000051129e-05,
|
70 |
"ep_info_buffer": {
|
71 |
":type:": "<class 'collections.deque'>",
|
72 |
+
":serialized:": "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"
|
73 |
},
|
74 |
"ep_success_buffer": {
|
75 |
":type:": "<class 'collections.deque'>",
|
76 |
":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
|
77 |
},
|
78 |
+
"_n_updates": 244144,
|
79 |
"n_steps": 1024,
|
80 |
"gamma": 0.999,
|
81 |
"gae_lambda": 0.98,
|
ppo-LunarLander-v2/policy.optimizer.pth
CHANGED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
size 87865
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:80b187db53b64d14c0271f484202fb3d0108676faa1f97df1f2e41510bada041
|
3 |
size 87865
|
ppo-LunarLander-v2/policy.pth
CHANGED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
size 43201
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:ce8a87bd6529b790d84c344b5f1e79793d22b400e85510e9a9b602c3308cc7e3
|
3 |
size 43201
|
results.json
CHANGED
@@ -1 +1 @@
|
|
1 |
-
{"mean_reward":
|
|
|
1 |
+
{"mean_reward": 284.5739908029501, "std_reward": 22.877836093355583, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-07T14:32:39.186653"}
|