PPO LunarLander v2 Trained
Browse files- README.md +36 -0
- config.json +1 -0
- ppo-LunarLander-v2.zip +3 -0
- ppo-LunarLander-v2/_stable_baselines3_version +1 -0
- ppo-LunarLander-v2/data +94 -0
- ppo-LunarLander-v2/policy.optimizer.pth +3 -0
- ppo-LunarLander-v2/policy.pth +3 -0
- ppo-LunarLander-v2/pytorch_variables.pth +3 -0
- ppo-LunarLander-v2/system_info.txt +7 -0
- replay.mp4 +0 -0
- results.json +1 -0
README.md
ADDED
|
@@ -0,0 +1,36 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
library_name: stable-baselines3
|
| 3 |
+
tags:
|
| 4 |
+
- LunarLander-v2
|
| 5 |
+
- deep-reinforcement-learning
|
| 6 |
+
- reinforcement-learning
|
| 7 |
+
- stable-baselines3
|
| 8 |
+
model-index:
|
| 9 |
+
- name: PPO
|
| 10 |
+
results:
|
| 11 |
+
- metrics:
|
| 12 |
+
- type: mean_reward
|
| 13 |
+
value: 269.85 +/- 26.72
|
| 14 |
+
name: mean_reward
|
| 15 |
+
task:
|
| 16 |
+
type: reinforcement-learning
|
| 17 |
+
name: reinforcement-learning
|
| 18 |
+
dataset:
|
| 19 |
+
name: LunarLander-v2
|
| 20 |
+
type: LunarLander-v2
|
| 21 |
+
---
|
| 22 |
+
|
| 23 |
+
# **PPO** Agent playing **LunarLander-v2**
|
| 24 |
+
This is a trained model of a **PPO** agent playing **LunarLander-v2**
|
| 25 |
+
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
|
| 26 |
+
|
| 27 |
+
## Usage (with Stable-baselines3)
|
| 28 |
+
TODO: Add your code
|
| 29 |
+
|
| 30 |
+
|
| 31 |
+
```python
|
| 32 |
+
from stable_baselines3 import ...
|
| 33 |
+
from huggingface_sb3 import load_from_hub
|
| 34 |
+
|
| 35 |
+
...
|
| 36 |
+
```
|
config.json
ADDED
|
@@ -0,0 +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 0x7f66bf6d7950>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f66bf6d79e0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f66bf6d7a70>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f66bf6d7b00>", "_build": "<function ActorCriticPolicy._build at 0x7f66bf6d7b90>", "forward": "<function ActorCriticPolicy.forward at 0x7f66bf6d7c20>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f66bf6d7cb0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f66bf6d7d40>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f66bf6d7dd0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f66bf6d7e60>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f66bf6d7ef0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f66bf7267e0>"}, "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": 1507328, "_total_timesteps": 1500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1663064529.6438584, "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": -0.004885333333333408, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_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:": "gAWVwQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsBSwFLE0MEiABTAJROhZQpjAFflIWUjEgvdXNyL2xvY2FsL2xpYi9weXRob24zLjcvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuAQwIAAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEgvdXNyL2xvY2FsL2xpYi9weXRob24zLjcvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUdU5OaACMEF9tYWtlX2VtcHR5X2NlbGyUk5QpUpSFlHSUUpSMHGNsb3VkcGlja2xlLmNsb3VkcGlja2xlX2Zhc3SUjBJfZnVuY3Rpb25fc2V0c3RhdGWUk5RoH32UfZQoaBZoDYwMX19xdWFsbmFtZV9flIwZY29uc3RhbnRfZm4uPGxvY2Fscz4uZnVuY5SMD19fYW5ub3RhdGlvbnNfX5R9lIwOX19rd2RlZmF1bHRzX1+UTowMX19kZWZhdWx0c19flE6MCl9fbW9kdWxlX1+UaBeMB19fZG9jX1+UTowLX19jbG9zdXJlX1+UaACMCl9tYWtlX2NlbGyUk5RHP8mZmZmZmZqFlFKUhZSMF19jbG91ZHBpY2tsZV9zdWJtb2R1bGVzlF2UjAtfX2dsb2JhbHNfX5R9lHWGlIZSMC4="}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.10.133+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Fri Aug 26 08:44:51 UTC 2022", "Python": "3.7.13", "Stable-Baselines3": "1.6.0", "PyTorch": "1.12.1+cu113", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}
|
ppo-LunarLander-v2.zip
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:c037ff23bea4f244d67f47b0711e2898402757bb50b6cb03cfd985add376f420
|
| 3 |
+
size 147055
|
ppo-LunarLander-v2/_stable_baselines3_version
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
1.6.0
|
ppo-LunarLander-v2/data
ADDED
|
@@ -0,0 +1,94 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"policy_class": {
|
| 3 |
+
":type:": "<class 'abc.ABCMeta'>",
|
| 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 0x7f66bf6d7950>",
|
| 8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f66bf6d79e0>",
|
| 9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f66bf6d7a70>",
|
| 10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f66bf6d7b00>",
|
| 11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7f66bf6d7b90>",
|
| 12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7f66bf6d7c20>",
|
| 13 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f66bf6d7cb0>",
|
| 14 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7f66bf6d7d40>",
|
| 15 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f66bf6d7dd0>",
|
| 16 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f66bf6d7e60>",
|
| 17 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7f66bf6d7ef0>",
|
| 18 |
+
"__abstractmethods__": "frozenset()",
|
| 19 |
+
"_abc_impl": "<_abc_data object at 0x7f66bf7267e0>"
|
| 20 |
+
},
|
| 21 |
+
"verbose": 1,
|
| 22 |
+
"policy_kwargs": {},
|
| 23 |
+
"observation_space": {
|
| 24 |
+
":type:": "<class 'gym.spaces.box.Box'>",
|
| 25 |
+
":serialized:": "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",
|
| 26 |
+
"dtype": "float32",
|
| 27 |
+
"_shape": [
|
| 28 |
+
8
|
| 29 |
+
],
|
| 30 |
+
"low": "[-inf -inf -inf -inf -inf -inf -inf -inf]",
|
| 31 |
+
"high": "[inf inf inf inf inf inf inf inf]",
|
| 32 |
+
"bounded_below": "[False False False False False False False False]",
|
| 33 |
+
"bounded_above": "[False False False False False False False False]",
|
| 34 |
+
"_np_random": null
|
| 35 |
+
},
|
| 36 |
+
"action_space": {
|
| 37 |
+
":type:": "<class 'gym.spaces.discrete.Discrete'>",
|
| 38 |
+
":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
|
| 39 |
+
"n": 4,
|
| 40 |
+
"_shape": [],
|
| 41 |
+
"dtype": "int64",
|
| 42 |
+
"_np_random": null
|
| 43 |
+
},
|
| 44 |
+
"n_envs": 16,
|
| 45 |
+
"num_timesteps": 1507328,
|
| 46 |
+
"_total_timesteps": 1500000,
|
| 47 |
+
"_num_timesteps_at_start": 0,
|
| 48 |
+
"seed": null,
|
| 49 |
+
"action_noise": null,
|
| 50 |
+
"start_time": 1663064529.6438584,
|
| 51 |
+
"learning_rate": 0.0003,
|
| 52 |
+
"tensorboard_log": null,
|
| 53 |
+
"lr_schedule": {
|
| 54 |
+
":type:": "<class 'function'>",
|
| 55 |
+
":serialized:": "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"
|
| 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'>",
|
| 63 |
+
":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="
|
| 64 |
+
},
|
| 65 |
+
"_last_original_obs": null,
|
| 66 |
+
"_episode_num": 0,
|
| 67 |
+
"use_sde": false,
|
| 68 |
+
"sde_sample_freq": -1,
|
| 69 |
+
"_current_progress_remaining": -0.004885333333333408,
|
| 70 |
+
"ep_info_buffer": {
|
| 71 |
+
":type:": "<class 'collections.deque'>",
|
| 72 |
+
":serialized:": "gAWVNBAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMI7GexFAkTcECUhpRSlIwBbJRNAgGMAXSUR0CV3O8TBZZCdX2UKGgGaAloD0MIZLFNKlrBcECUhpRSlGgVS/5oFkdAld1gUYbbUXV9lChoBmgJaA9DCJvJN9tcAHNAlIaUUpRoFUvvaBZHQJXdkW2w3YN1fZQoaAZoCWgPQwh2VDVBlERxQJSGlFKUaBVL7GgWR0CV3a6OHWSVdX2UKGgGaAloD0MI1a90PryZc0CUhpRSlGgVS+doFkdAld66GHpKSXV9lChoBmgJaA9DCERPyqSGdnJAlIaUUpRoFUvRaBZHQJXe7hm5Dqp1fZQoaAZoCWgPQwiimpKsg89xQJSGlFKUaBVNIQJoFkdAld+IqCpWFXV9lChoBmgJaA9DCI/DYP5K+nJAlIaUUpRoFU0bAWgWR0CV4ChWYF7ldX2UKGgGaAloD0MIfHxCdt6cb0CUhpRSlGgVS89oFkdAleA7HuJDV3V9lChoBmgJaA9DCEMDsWymK3JAlIaUUpRoFUvLaBZHQJXgQR28qWl1fZQoaAZoCWgPQwg9R+S7FMdyQJSGlFKUaBVNBAFoFkdAleDPfbblBHV9lChoBmgJaA9DCOsB85Bpg3FAlIaUUpRoFU0CAWgWR0CV4OFWn0kGdX2UKGgGaAloD0MIB7ZKsHjScECUhpRSlGgVS9doFkdAleGR0hePaXV9lChoBmgJaA9DCDE/NzSlKHJAlIaUUpRoFUvXaBZHQJXhszFdcB51fZQoaAZoCWgPQwhOJm4VxFFxQJSGlFKUaBVL+GgWR0CV4ecJdB0IdX2UKGgGaAloD0MI/g3aq88ucECUhpRSlGgVTRYBaBZHQJXiAs4DLbJ1fZQoaAZoCWgPQwg9m1Wfqz5wQJSGlFKUaBVL6mgWR0CV4jIV/MGHdX2UKGgGaAloD0MIdT+nID9fcECUhpRSlGgVS+BoFkdAleJlmz0HyHV9lChoBmgJaA9DCEXZW8q5c3NAlIaUUpRoFUvqaBZHQJXizCpFTeh1fZQoaAZoCWgPQwgRct7/x9JzQJSGlFKUaBVNJQFoFkdAleRNVmz0H3V9lChoBmgJaA9DCFH6Qsi5K3FAlIaUUpRoFUvwaBZHQJXkW+qR2bJ1fZQoaAZoCWgPQwgSpb3BF3FxQJSGlFKUaBVLzmgWR0CV5PpMHryEdX2UKGgGaAloD0MI0UAsm/nkcUCUhpRSlGgVS/ZoFkdAleXwgTyrgnV9lChoBmgJaA9DCBqIZTMHznBAlIaUUpRoFUv6aBZHQJXmKx3V0911fZQoaAZoCWgPQwhXzt4Z7WhxQJSGlFKUaBVL6mgWR0CV5nB4D9wWdX2UKGgGaAloD0MIdPBMaBKDcUCUhpRSlGgVTSwBaBZHQJXmshs67ul1fZQoaAZoCWgPQwgMy59vC+pwQJSGlFKUaBVL+GgWR0CV5ufEGZ/kdX2UKGgGaAloD0MIFlETfX5dckCUhpRSlGgVS99oFkdAleckmICU5nV9lChoBmgJaA9DCIZ0eAgjrXJAlIaUUpRoFUv3aBZHQJXnnFjurp91fZQoaAZoCWgPQwhfmbfqOhxuQJSGlFKUaBVL62gWR0CV56hew9q2dX2UKGgGaAloD0MI9DY2O9KXb0CUhpRSlGgVS9VoFkdAleezFuNxVHV9lChoBmgJaA9DCCP5SiDlSXFAlIaUUpRoFUv1aBZHQJXoK8oQWep1fZQoaAZoCWgPQwhV+glntyBzQJSGlFKUaBVL8WgWR0CV6LzEaVD8dX2UKGgGaAloD0MIWU+tvro/ckCUhpRSlGgVTSUBaBZHQJXpEA3kxRF1fZQoaAZoCWgPQwgVjErqxLVyQJSGlFKUaBVL+WgWR0CV6oUliSaFdX2UKGgGaAloD0MIBmUaTS5+cUCUhpRSlGgVTf8BaBZHQJX9vS0BwMp1fZQoaAZoCWgPQwhv10tTRDtyQJSGlFKUaBVL0WgWR0CV/mi/fwZwdX2UKGgGaAloD0MI647FNqnAcUCUhpRSlGgVS+VoFkdAlf66gdwNsnV9lChoBmgJaA9DCDRMbamDhnNAlIaUUpRoFUvhaBZHQJX/IjxCpm51fZQoaAZoCWgPQwicoiO5fENyQJSGlFKUaBVL0mgWR0CV/ztmthd/dX2UKGgGaAloD0MIndSXpR0nb0CUhpRSlGgVTQEBaBZHQJX/TymQ8wJ1fZQoaAZoCWgPQwj3yycrxhxyQJSGlFKUaBVL9mgWR0CV/+AZsKsudX2UKGgGaAloD0MIfQiqRu+lc0CUhpRSlGgVS9xoFkdAlgAAEQoTf3V9lChoBmgJaA9DCNPB+j/HBnJAlIaUUpRoFUvmaBZHQJYAT3BYV7B1fZQoaAZoCWgPQwgdke9S6mpxQJSGlFKUaBVL12gWR0CWAYjIJZ4fdX2UKGgGaAloD0MIJCU9DO08cUCUhpRSlGgVS+ZoFkdAlgGQVoHs1XV9lChoBmgJaA9DCJ54zhYQu3FAlIaUUpRoFU0oAWgWR0CWAfaZQYUGdX2UKGgGaAloD0MI8ia/RWewckCUhpRSlGgVS91oFkdAlgO4vvjOs3V9lChoBmgJaA9DCI6vPbOkGHNAlIaUUpRoFUvXaBZHQJYERFuvUz91fZQoaAZoCWgPQwimRX2SuzVtQJSGlFKUaBVNDQFoFkdAlgSwHRkVe3V9lChoBmgJaA9DCOdxGMwfhnJAlIaUUpRoFUvoaBZHQJYFGbZvkzZ1fZQoaAZoCWgPQwgMeQQ3UqdxQJSGlFKUaBVL2mgWR0CWBTCQLeANdX2UKGgGaAloD0MI/dzQlJ2yb0CUhpRSlGgVS+VoFkdAlgWRciW3SnV9lChoBmgJaA9DCBiYFYp0/nNAlIaUUpRoFU0sAmgWR0CWBdMZgogFdX2UKGgGaAloD0MIkZkLXB5NcUCUhpRSlGgVS9doFkdAlgXTySV4YHV9lChoBmgJaA9DCHbCS3BqAHJAlIaUUpRoFUvZaBZHQJYF/5AQg9x1fZQoaAZoCWgPQwiP3nAfuRtwQJSGlFKUaBVL32gWR0CWBmnRLK3edX2UKGgGaAloD0MIrir7rsjpcUCUhpRSlGgVS9doFkdAlgdiidrftXV9lChoBmgJaA9DCCO6Z12jzWxAlIaUUpRoFUvVaBZHQJYHu9FnZkF1fZQoaAZoCWgPQwgX9N4YwjhwQJSGlFKUaBVNFgFoFkdAlgkJC0F8onV9lChoBmgJaA9DCPopjgMvanBAlIaUUpRoFUvaaBZHQJYKEb+98JF1fZQoaAZoCWgPQwifdY2WAyFwQJSGlFKUaBVL+GgWR0CWCmS1E3KkdX2UKGgGaAloD0MI424QrRVgcUCUhpRSlGgVS+loFkdAlgrsaOxSpHV9lChoBmgJaA9DCAMmcOtul3FAlIaUUpRoFUvZaBZHQJYK9Lytmth1fZQoaAZoCWgPQwiJRQw7jENuQJSGlFKUaBVL4GgWR0CWCxGmUGFBdX2UKGgGaAloD0MIYAFMGXgWckCUhpRSlGgVS9BoFkdAlgv+/gzguXV9lChoBmgJaA9DCAbX3NE/MnJAlIaUUpRoFUv0aBZHQJYMDWd3B551fZQoaAZoCWgPQwiJ1LSLqW5wQJSGlFKUaBVL5WgWR0CWDBR+SbH7dX2UKGgGaAloD0MIIjgu46ZlbkCUhpRSlGgVS+1oFkdAlgwaoESuhnV9lChoBmgJaA9DCKDAO/m0HXFAlIaUUpRoFU0RAWgWR0CWDO7DVH4HdX2UKGgGaAloD0MI68n8o6+cckCUhpRSlGgVTQ4CaBZHQJYNI9V3ljp1fZQoaAZoCWgPQwjdJtwr8y5CQJSGlFKUaBVLqmgWR0CWDXwMH8jzdX2UKGgGaAloD0MIqMMKt/zAb0CUhpRSlGgVTVsDaBZHQJYOOk+HJtB1fZQoaAZoCWgPQwhIqYQndH9wQJSGlFKUaBVNHgFoFkdAlg7D3yqdYnV9lChoBmgJaA9DCGdHqu88anBAlIaUUpRoFU0XAWgWR0CWDu4RVZLadX2UKGgGaAloD0MI0cq9wKz1b0CUhpRSlGgVS9JoFkdAlg+keU6gd3V9lChoBmgJaA9DCEuPpnoy6XFAlIaUUpRoFU0bA2gWR0CWD7zI3irDdX2UKGgGaAloD0MI4GdcOJA7ckCUhpRSlGgVS8ZoFkdAlg/KU7jkuHV9lChoBmgJaA9DCNKJBFNNcW9AlIaUUpRoFUvSaBZHQJYQGFcpsoF1fZQoaAZoCWgPQwhj7ISX4FhLQJSGlFKUaBVLuWgWR0CWEHFMIu5CdX2UKGgGaAloD0MIpyVWRiN7cECUhpRSlGgVS+xoFkdAlhC/4ubqhXV9lChoBmgJaA9DCGSw4lTrUm1AlIaUUpRoFUvMaBZHQJYQ0XO4XoF1fZQoaAZoCWgPQwiFtMagUwtzQJSGlFKUaBVNIgFoFkdAlhEtIwudw3V9lChoBmgJaA9DCCiBzTk4InNAlIaUUpRoFUvaaBZHQJYRLcpLEk11fZQoaAZoCWgPQwgUCDvFqvRwQJSGlFKUaBVL7WgWR0CWEZMAFPi2dX2UKGgGaAloD0MITfc6qW9sckCUhpRSlGgVS+ZoFkdAlhIsLjPv8nV9lChoBmgJaA9DCA8qcR3jT3JAlIaUUpRoFUvuaBZHQJYSin+AEuB1fZQoaAZoCWgPQwi296kqtB5tQJSGlFKUaBVL5WgWR0CWEquez2OAdX2UKGgGaAloD0MID0bsE8ALcUCUhpRSlGgVS9RoFkdAlhOneSB9TnV9lChoBmgJaA9DCDCd1m3QlnNAlIaUUpRoFUv2aBZHQJYTxz5oGpx1fZQoaAZoCWgPQwgMHqZ9MxFwQJSGlFKUaBVL4GgWR0CWE8dJaq0ddX2UKGgGaAloD0MITU2CN6SkcECUhpRSlGgVS9JoFkdAlhRFG9YfXHV9lChoBmgJaA9DCHGNz2R/KHNAlIaUUpRoFUvJaBZHQJYUgQd0aIh1fZQoaAZoCWgPQwiLNse5TbhxQJSGlFKUaBVL5GgWR0CWFMoRZlnRdX2UKGgGaAloD0MIURToE/k5b0CUhpRSlGgVS9VoFkdAlhUlfzBhyHV9lChoBmgJaA9DCNkj1AzpwnJAlIaUUpRoFU0HAWgWR0CWFadLg4wRdX2UKGgGaAloD0MIqu6RzVVRcUCUhpRSlGgVS9JoFkdAlhXlfNRm9XV9lChoBmgJaA9DCI3xYfbyh3JAlIaUUpRoFUvkaBZHQJYV69CeEqV1fZQoaAZoCWgPQwhNMnIWNtJxQJSGlFKUaBVL2GgWR0CWFgdmQKa5dX2UKGgGaAloD0MIxXQhVn90cECUhpRSlGgVS/BoFkdAlhYcC5mRNnVlLg=="
|
| 73 |
+
},
|
| 74 |
+
"ep_success_buffer": {
|
| 75 |
+
":type:": "<class 'collections.deque'>",
|
| 76 |
+
":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
|
| 77 |
+
},
|
| 78 |
+
"_n_updates": 368,
|
| 79 |
+
"n_steps": 1024,
|
| 80 |
+
"gamma": 0.999,
|
| 81 |
+
"gae_lambda": 0.98,
|
| 82 |
+
"ent_coef": 0.01,
|
| 83 |
+
"vf_coef": 0.5,
|
| 84 |
+
"max_grad_norm": 0.5,
|
| 85 |
+
"batch_size": 64,
|
| 86 |
+
"n_epochs": 4,
|
| 87 |
+
"clip_range": {
|
| 88 |
+
":type:": "<class 'function'>",
|
| 89 |
+
":serialized:": "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"
|
| 90 |
+
},
|
| 91 |
+
"clip_range_vf": null,
|
| 92 |
+
"normalize_advantage": true,
|
| 93 |
+
"target_kl": null
|
| 94 |
+
}
|
ppo-LunarLander-v2/policy.optimizer.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:7d396c766bf8df3fe6d5253d56610b2bfbddc32acc6b5adcfa659cb14d723663
|
| 3 |
+
size 87865
|
ppo-LunarLander-v2/policy.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:d924aa80ca9f02d3fb353fa6910ed979051ec14a2be45f3ec44c9ff36cdb6219
|
| 3 |
+
size 43201
|
ppo-LunarLander-v2/pytorch_variables.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
|
| 3 |
+
size 431
|
ppo-LunarLander-v2/system_info.txt
ADDED
|
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
OS: Linux-5.10.133+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Fri Aug 26 08:44:51 UTC 2022
|
| 2 |
+
Python: 3.7.13
|
| 3 |
+
Stable-Baselines3: 1.6.0
|
| 4 |
+
PyTorch: 1.12.1+cu113
|
| 5 |
+
GPU Enabled: True
|
| 6 |
+
Numpy: 1.21.6
|
| 7 |
+
Gym: 0.21.0
|
replay.mp4
ADDED
|
Binary file (193 kB). View file
|
|
|
results.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"mean_reward": 269.8532324143427, "std_reward": 26.721259928890753, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-09-13T10:46:50.192666"}
|