HELLO UNit1
Browse files- README.md +37 -0
- config.json +1 -0
- ppo_model.zip +3 -0
- ppo_model/_stable_baselines3_version +1 -0
- ppo_model/data +95 -0
- ppo_model/policy.optimizer.pth +3 -0
- ppo_model/policy.pth +3 -0
- ppo_model/pytorch_variables.pth +3 -0
- ppo_model/system_info.txt +7 -0
- replay.mp4 +0 -0
- results.json +1 -0
README.md
ADDED
|
@@ -0,0 +1,37 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 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 |
+
- task:
|
| 12 |
+
type: reinforcement-learning
|
| 13 |
+
name: reinforcement-learning
|
| 14 |
+
dataset:
|
| 15 |
+
name: LunarLander-v2
|
| 16 |
+
type: LunarLander-v2
|
| 17 |
+
metrics:
|
| 18 |
+
- type: mean_reward
|
| 19 |
+
value: 263.22 +/- 18.50
|
| 20 |
+
name: mean_reward
|
| 21 |
+
verified: false
|
| 22 |
+
---
|
| 23 |
+
|
| 24 |
+
# **PPO** Agent playing **LunarLander-v2**
|
| 25 |
+
This is a trained model of a **PPO** agent playing **LunarLander-v2**
|
| 26 |
+
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
|
| 27 |
+
|
| 28 |
+
## Usage (with Stable-baselines3)
|
| 29 |
+
TODO: Add your code
|
| 30 |
+
|
| 31 |
+
|
| 32 |
+
```python
|
| 33 |
+
from stable_baselines3 import ...
|
| 34 |
+
from huggingface_sb3 import load_from_hub
|
| 35 |
+
|
| 36 |
+
...
|
| 37 |
+
```
|
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 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 0x7ff05b1ddee0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ff05b1ddf70>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ff05b1e1040>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ff05b1e10d0>", "_build": "<function ActorCriticPolicy._build at 0x7ff05b1e1160>", "forward": "<function ActorCriticPolicy.forward at 0x7ff05b1e11f0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7ff05b1e1280>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ff05b1e1310>", "_predict": "<function ActorCriticPolicy._predict at 0x7ff05b1e13a0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ff05b1e1430>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ff05b1e14c0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ff05b1e1550>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7ff05b1dfc40>"}, "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": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1678819933567644630, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOS9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOS9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/M6kqMFUyYYWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="}, "_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": -0.015808000000000044, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 248, "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:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOS9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOS9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/yZmZmZmZmoWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.9.16", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}
|
ppo_model.zip
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:adb8b3301381937fb0cf304117134d0546fbdac20d4fd143fe94ac5f45ab809e
|
| 3 |
+
size 147409
|
ppo_model/_stable_baselines3_version
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
1.7.0
|
ppo_model/data
ADDED
|
@@ -0,0 +1,95 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 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 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 ",
|
| 7 |
+
"__init__": "<function ActorCriticPolicy.__init__ at 0x7ff05b1ddee0>",
|
| 8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ff05b1ddf70>",
|
| 9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ff05b1e1040>",
|
| 10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ff05b1e10d0>",
|
| 11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7ff05b1e1160>",
|
| 12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7ff05b1e11f0>",
|
| 13 |
+
"extract_features": "<function ActorCriticPolicy.extract_features at 0x7ff05b1e1280>",
|
| 14 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ff05b1e1310>",
|
| 15 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7ff05b1e13a0>",
|
| 16 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ff05b1e1430>",
|
| 17 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ff05b1e14c0>",
|
| 18 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7ff05b1e1550>",
|
| 19 |
+
"__abstractmethods__": "frozenset()",
|
| 20 |
+
"_abc_impl": "<_abc._abc_data object at 0x7ff05b1dfc40>"
|
| 21 |
+
},
|
| 22 |
+
"verbose": 1,
|
| 23 |
+
"policy_kwargs": {},
|
| 24 |
+
"observation_space": {
|
| 25 |
+
":type:": "<class 'gym.spaces.box.Box'>",
|
| 26 |
+
":serialized:": "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",
|
| 27 |
+
"dtype": "float32",
|
| 28 |
+
"_shape": [
|
| 29 |
+
8
|
| 30 |
+
],
|
| 31 |
+
"low": "[-inf -inf -inf -inf -inf -inf -inf -inf]",
|
| 32 |
+
"high": "[inf inf inf inf inf inf inf inf]",
|
| 33 |
+
"bounded_below": "[False False False False False False False False]",
|
| 34 |
+
"bounded_above": "[False False False False False False False False]",
|
| 35 |
+
"_np_random": null
|
| 36 |
+
},
|
| 37 |
+
"action_space": {
|
| 38 |
+
":type:": "<class 'gym.spaces.discrete.Discrete'>",
|
| 39 |
+
":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
|
| 40 |
+
"n": 4,
|
| 41 |
+
"_shape": [],
|
| 42 |
+
"dtype": "int64",
|
| 43 |
+
"_np_random": null
|
| 44 |
+
},
|
| 45 |
+
"n_envs": 16,
|
| 46 |
+
"num_timesteps": 1015808,
|
| 47 |
+
"_total_timesteps": 1000000,
|
| 48 |
+
"_num_timesteps_at_start": 0,
|
| 49 |
+
"seed": null,
|
| 50 |
+
"action_noise": null,
|
| 51 |
+
"start_time": 1678819933567644630,
|
| 52 |
+
"learning_rate": 0.0003,
|
| 53 |
+
"tensorboard_log": null,
|
| 54 |
+
"lr_schedule": {
|
| 55 |
+
":type:": "<class 'function'>",
|
| 56 |
+
":serialized:": "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"
|
| 57 |
+
},
|
| 58 |
+
"_last_obs": {
|
| 59 |
+
":type:": "<class 'numpy.ndarray'>",
|
| 60 |
+
":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAADOfHr2upYm6yOMXOJNAGDMYfr26FzwxtwAAgD8AAIA/U8MivsAD2D4Na04+dS+zvuv4hTz8nIM9AAAAAAAAAADNf1M92g1ZPwPN3716WcK+2tKLPT6EaL0AAAAAAAAAAJpXpD0Zncw+8gLGvX5Ytb7GVc48ZNpKvQAAAAAAAAAAAPU0vWdoPj/xAB+9MVrQvt4AAjw+juC8AAAAAAAAAACmfou94eSruhMxQbn8QzO0bc2XOR5uXTgAAIA/AACAP3Pmtj0/5lY/FU88PUvxyL7QmwQ+HiamPAAAAAAAAAAAzd7ovIlhRD5eIhC9EhxxvpdLyLzG/Qg9AAAAAAAAAABAqqC96ZEGPbfdPT7LGWe+76TsvJrPVj0AAAAAAAAAALMsDb0wYJk+JCTJu1Szc77dp667whqjPAAAAAAAAAAABpSrPiNsSD9IGoW+PJ7PvkvhRT5oBJi+AAAAAAAAAABm9+g8w01LukD1GDqriJW2KB6cu4nzMbkAAIA/AACAP0CHxr1Raoo/zWw5vpCA1b48Y5O99SabvQAAAAAAAAAAgKtTPoPlLj9X7S69EaLHvvfTBD5jmN68AAAAAAAAAABmQqS8aUk4PY0uaT4VXVC++nsKPjZhpTwAAAAAAAAAAGZy4jxxvS08A0cyu6RFH76ZWcK8OaqpPAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksQSwiGlIwBQ5R0lFKULg=="
|
| 61 |
+
},
|
| 62 |
+
"_last_episode_starts": {
|
| 63 |
+
":type:": "<class 'numpy.ndarray'>",
|
| 64 |
+
":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="
|
| 65 |
+
},
|
| 66 |
+
"_last_original_obs": null,
|
| 67 |
+
"_episode_num": 0,
|
| 68 |
+
"use_sde": false,
|
| 69 |
+
"sde_sample_freq": -1,
|
| 70 |
+
"_current_progress_remaining": -0.015808000000000044,
|
| 71 |
+
"ep_info_buffer": {
|
| 72 |
+
":type:": "<class 'collections.deque'>",
|
| 73 |
+
":serialized:": "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"
|
| 74 |
+
},
|
| 75 |
+
"ep_success_buffer": {
|
| 76 |
+
":type:": "<class 'collections.deque'>",
|
| 77 |
+
":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
|
| 78 |
+
},
|
| 79 |
+
"_n_updates": 248,
|
| 80 |
+
"n_steps": 1024,
|
| 81 |
+
"gamma": 0.999,
|
| 82 |
+
"gae_lambda": 0.98,
|
| 83 |
+
"ent_coef": 0.01,
|
| 84 |
+
"vf_coef": 0.5,
|
| 85 |
+
"max_grad_norm": 0.5,
|
| 86 |
+
"batch_size": 64,
|
| 87 |
+
"n_epochs": 4,
|
| 88 |
+
"clip_range": {
|
| 89 |
+
":type:": "<class 'function'>",
|
| 90 |
+
":serialized:": "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"
|
| 91 |
+
},
|
| 92 |
+
"clip_range_vf": null,
|
| 93 |
+
"normalize_advantage": true,
|
| 94 |
+
"target_kl": null
|
| 95 |
+
}
|
ppo_model/policy.optimizer.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:65329656a84fd9a5690e4fbc2d7164b1d6e903772dbe60ff189851a19afd2476
|
| 3 |
+
size 87929
|
ppo_model/policy.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:0d8ec6fbeecfda399ed44441f423919a7c17689ccfe2a0b250c692fbd41b2f4b
|
| 3 |
+
size 43393
|
ppo_model/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_model/system_info.txt
ADDED
|
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
- OS: Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
|
| 2 |
+
- Python: 3.9.16
|
| 3 |
+
- Stable-Baselines3: 1.7.0
|
| 4 |
+
- PyTorch: 1.13.1+cu116
|
| 5 |
+
- GPU Enabled: True
|
| 6 |
+
- Numpy: 1.22.4
|
| 7 |
+
- Gym: 0.21.0
|
replay.mp4
ADDED
|
Binary file (238 kB). View file
|
|
|
results.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"mean_reward": 263.22179987296465, "std_reward": 18.503375488403105, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-14T19:45:00.893529"}
|