First PPO LunarLander-v2 trained agent, with around 30 million timsteps
Browse files- .gitattributes +1 -0
- README.md +37 -0
- config.json +1 -0
- ppo-LunarLander-v2.zip +3 -0
- ppo-LunarLander-v2/_stable_baselines3_version +1 -0
- ppo-LunarLander-v2/data +99 -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 +9 -0
- replay.mp4 +3 -0
- results.json +1 -0
.gitattributes
CHANGED
|
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
|
| 33 |
*.zip filter=lfs diff=lfs merge=lfs -text
|
| 34 |
*.zst filter=lfs diff=lfs merge=lfs -text
|
| 35 |
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
|
|
|
|
|
| 33 |
*.zip filter=lfs diff=lfs merge=lfs -text
|
| 34 |
*.zst filter=lfs diff=lfs merge=lfs -text
|
| 35 |
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
| 36 |
+
replay.mp4 filter=lfs diff=lfs merge=lfs -text
|
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: 278.71 +/- 18.68
|
| 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 0x79da5e593ec0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x79da5e593f60>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x79da5e59c040>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x79da5e59c0e0>", "_build": "<function ActorCriticPolicy._build at 0x79da5e59c180>", "forward": "<function ActorCriticPolicy.forward at 0x79da5e59c220>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x79da5e59c2c0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x79da5e59c360>", "_predict": "<function ActorCriticPolicy._predict at 0x79da5e59c400>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x79da5e59c4a0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x79da5e59c540>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x79da5e59c5e0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x79da5e523940>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 3112960, "_total_timesteps": 10000000.0, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1739131487676770694, "learning_rate": 0.0003, "tensorboard_log": null, "_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": 0.688704, "_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": 946, "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:": "gAWV2wAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgOjApfbnBfcmFuZG9tlE51Yi4=", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "n_steps": 2048, "gamma": 0.99, "gae_lambda": 0.95, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 10, "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:": "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"}, "system_info": {"OS": "Linux-6.1.85+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Thu Jun 27 21:05:47 UTC 2024", "Python": "3.11.11", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.5.1+cu124", "GPU Enabled": "True", "Numpy": "1.26.4", "Cloudpickle": "3.1.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}
|
ppo-LunarLander-v2.zip
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:2e881ff4a36f9105f6a7ce3580a07a01fded301e47eac25c472c5e20caeb148d
|
| 3 |
+
size 147989
|
ppo-LunarLander-v2/_stable_baselines3_version
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
2.0.0a5
|
ppo-LunarLander-v2/data
ADDED
|
@@ -0,0 +1,99 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 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 0x79da5e593ec0>",
|
| 8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x79da5e593f60>",
|
| 9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x79da5e59c040>",
|
| 10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x79da5e59c0e0>",
|
| 11 |
+
"_build": "<function ActorCriticPolicy._build at 0x79da5e59c180>",
|
| 12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x79da5e59c220>",
|
| 13 |
+
"extract_features": "<function ActorCriticPolicy.extract_features at 0x79da5e59c2c0>",
|
| 14 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x79da5e59c360>",
|
| 15 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x79da5e59c400>",
|
| 16 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x79da5e59c4a0>",
|
| 17 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x79da5e59c540>",
|
| 18 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x79da5e59c5e0>",
|
| 19 |
+
"__abstractmethods__": "frozenset()",
|
| 20 |
+
"_abc_impl": "<_abc._abc_data object at 0x79da5e523940>"
|
| 21 |
+
},
|
| 22 |
+
"verbose": 1,
|
| 23 |
+
"policy_kwargs": {},
|
| 24 |
+
"num_timesteps": 3112960,
|
| 25 |
+
"_total_timesteps": 10000000.0,
|
| 26 |
+
"_num_timesteps_at_start": 0,
|
| 27 |
+
"seed": null,
|
| 28 |
+
"action_noise": null,
|
| 29 |
+
"start_time": 1739131487676770694,
|
| 30 |
+
"learning_rate": 0.0003,
|
| 31 |
+
"tensorboard_log": null,
|
| 32 |
+
"_last_obs": {
|
| 33 |
+
":type:": "<class 'numpy.ndarray'>",
|
| 34 |
+
":serialized:": "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"
|
| 35 |
+
},
|
| 36 |
+
"_last_episode_starts": {
|
| 37 |
+
":type:": "<class 'numpy.ndarray'>",
|
| 38 |
+
":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="
|
| 39 |
+
},
|
| 40 |
+
"_last_original_obs": null,
|
| 41 |
+
"_episode_num": 0,
|
| 42 |
+
"use_sde": false,
|
| 43 |
+
"sde_sample_freq": -1,
|
| 44 |
+
"_current_progress_remaining": 0.688704,
|
| 45 |
+
"_stats_window_size": 100,
|
| 46 |
+
"ep_info_buffer": {
|
| 47 |
+
":type:": "<class 'collections.deque'>",
|
| 48 |
+
":serialized:": "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"
|
| 49 |
+
},
|
| 50 |
+
"ep_success_buffer": {
|
| 51 |
+
":type:": "<class 'collections.deque'>",
|
| 52 |
+
":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
|
| 53 |
+
},
|
| 54 |
+
"_n_updates": 946,
|
| 55 |
+
"observation_space": {
|
| 56 |
+
":type:": "<class 'gymnasium.spaces.box.Box'>",
|
| 57 |
+
":serialized:": "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",
|
| 58 |
+
"dtype": "float32",
|
| 59 |
+
"bounded_below": "[ True True True True True True True True]",
|
| 60 |
+
"bounded_above": "[ True True True True True True True True]",
|
| 61 |
+
"_shape": [
|
| 62 |
+
8
|
| 63 |
+
],
|
| 64 |
+
"low": "[-90. -90. -5. -5. -3.1415927 -5.\n -0. -0. ]",
|
| 65 |
+
"high": "[90. 90. 5. 5. 3.1415927 5.\n 1. 1. ]",
|
| 66 |
+
"low_repr": "[-90. -90. -5. -5. -3.1415927 -5.\n -0. -0. ]",
|
| 67 |
+
"high_repr": "[90. 90. 5. 5. 3.1415927 5.\n 1. 1. ]",
|
| 68 |
+
"_np_random": null
|
| 69 |
+
},
|
| 70 |
+
"action_space": {
|
| 71 |
+
":type:": "<class 'gymnasium.spaces.discrete.Discrete'>",
|
| 72 |
+
":serialized:": "gAWV2wAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgOjApfbnBfcmFuZG9tlE51Yi4=",
|
| 73 |
+
"n": "4",
|
| 74 |
+
"start": "0",
|
| 75 |
+
"_shape": [],
|
| 76 |
+
"dtype": "int64",
|
| 77 |
+
"_np_random": null
|
| 78 |
+
},
|
| 79 |
+
"n_envs": 16,
|
| 80 |
+
"n_steps": 2048,
|
| 81 |
+
"gamma": 0.99,
|
| 82 |
+
"gae_lambda": 0.95,
|
| 83 |
+
"ent_coef": 0.0,
|
| 84 |
+
"vf_coef": 0.5,
|
| 85 |
+
"max_grad_norm": 0.5,
|
| 86 |
+
"batch_size": 64,
|
| 87 |
+
"n_epochs": 10,
|
| 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 |
+
"lr_schedule": {
|
| 96 |
+
":type:": "<class 'function'>",
|
| 97 |
+
":serialized:": "gAWV1gIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwiVAZcAiQFTAJROhZQpjAFflIWUjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjExL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lIwEZnVuY5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUS4RDCPiAANgPEogKlEMAlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTEvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUdU5OaACMEF9tYWtlX2VtcHR5X2NlbGyUk5QpUpSFlHSUUpRoAIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaCF9lH2UKGgYjARmdW5jlIwMX19xdWFsbmFtZV9flIwZY29uc3RhbnRfZm4uPGxvY2Fscz4uZnVuY5SMD19fYW5ub3RhdGlvbnNfX5R9lIwOX19rd2RlZmF1bHRzX1+UTowMX19kZWZhdWx0c19flE6MCl9fbW9kdWxlX1+UaBmMB19fZG9jX1+UTowLX19jbG9zdXJlX1+UaACMCl9tYWtlX2NlbGyUk5RHPzOpKjBVMmGFlFKUhZSMF19jbG91ZHBpY2tsZV9zdWJtb2R1bGVzlF2UjAtfX2dsb2JhbHNfX5R9lHWGlIZSMC4="
|
| 98 |
+
}
|
| 99 |
+
}
|
ppo-LunarLander-v2/policy.optimizer.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:79aca71bb92be0625f10f035b54b5fed4ae4511f69ac98c8479aef75ea361d1a
|
| 3 |
+
size 88362
|
ppo-LunarLander-v2/policy.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:50ae0894e1b9a446573fac82afec36d7b8de55b0f64fb44d70c3ef8900f81807
|
| 3 |
+
size 43762
|
ppo-LunarLander-v2/pytorch_variables.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:0c35cea3b2e60fb5e7e162d3592df775cd400e575a31c72f359fb9e654ab00c5
|
| 3 |
+
size 864
|
ppo-LunarLander-v2/system_info.txt
ADDED
|
@@ -0,0 +1,9 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
- OS: Linux-6.1.85+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Thu Jun 27 21:05:47 UTC 2024
|
| 2 |
+
- Python: 3.11.11
|
| 3 |
+
- Stable-Baselines3: 2.0.0a5
|
| 4 |
+
- PyTorch: 2.5.1+cu124
|
| 5 |
+
- GPU Enabled: True
|
| 6 |
+
- Numpy: 1.26.4
|
| 7 |
+
- Cloudpickle: 3.1.1
|
| 8 |
+
- Gymnasium: 0.28.1
|
| 9 |
+
- OpenAI Gym: 0.25.2
|
replay.mp4
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:cca429bc4ccc3643db1ac9a5135c90075634fd9596dcbb4139d8ff9dfd798a37
|
| 3 |
+
size 161740
|
results.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"mean_reward": 278.7098375, "std_reward": 18.681935052490804, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2025-02-09T21:13:40.586419"}
|