Initial commit
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: 257.02 +/- 17.29
|
| 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 0x784da9375b20>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x784da9375bc0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x784da9375c60>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x784da9375d00>", "_build": "<function ActorCriticPolicy._build at 0x784da9375da0>", "forward": "<function ActorCriticPolicy.forward at 0x784da9375e40>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x784da9375ee0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x784da9375f80>", "_predict": "<function ActorCriticPolicy._predict at 0x784da9376020>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x784da93760c0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x784da9376160>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x784da9376200>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x784da9f5f480>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1739229147931657801, "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": -0.015808000000000044, "_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": 310, "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:": "gAWV1gIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwiVAZcAiQFTAJROhZQpjAFflIWUjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjExL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lIwEZnVuY5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUS4RDCPiAANgPEogKlEMAlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTEvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUdU5OaACMEF9tYWtlX2VtcHR5X2NlbGyUk5QpUpSFlHSUUpRoAIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaCF9lH2UKGgYjARmdW5jlIwMX19xdWFsbmFtZV9flIwZY29uc3RhbnRfZm4uPGxvY2Fscz4uZnVuY5SMD19fYW5ub3RhdGlvbnNfX5R9lIwOX19rd2RlZmF1bHRzX1+UTowMX19kZWZhdWx0c19flE6MCl9fbW9kdWxlX1+UaBmMB19fZG9jX1+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-6.1.85+-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:d2553faea0e3b0dc1b0cf03b58eaa9544fd99f46fc3fda1ccc386b461c7d2472
|
| 3 |
+
size 148039
|
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 0x784da9375b20>",
|
| 8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x784da9375bc0>",
|
| 9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x784da9375c60>",
|
| 10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x784da9375d00>",
|
| 11 |
+
"_build": "<function ActorCriticPolicy._build at 0x784da9375da0>",
|
| 12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x784da9375e40>",
|
| 13 |
+
"extract_features": "<function ActorCriticPolicy.extract_features at 0x784da9375ee0>",
|
| 14 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x784da9375f80>",
|
| 15 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x784da9376020>",
|
| 16 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x784da93760c0>",
|
| 17 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x784da9376160>",
|
| 18 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x784da9376200>",
|
| 19 |
+
"__abstractmethods__": "frozenset()",
|
| 20 |
+
"_abc_impl": "<_abc._abc_data object at 0x784da9f5f480>"
|
| 21 |
+
},
|
| 22 |
+
"verbose": 1,
|
| 23 |
+
"policy_kwargs": {},
|
| 24 |
+
"num_timesteps": 1015808,
|
| 25 |
+
"_total_timesteps": 1000000,
|
| 26 |
+
"_num_timesteps_at_start": 0,
|
| 27 |
+
"seed": null,
|
| 28 |
+
"action_noise": null,
|
| 29 |
+
"start_time": 1739229147931657801,
|
| 30 |
+
"learning_rate": 0.0003,
|
| 31 |
+
"tensorboard_log": null,
|
| 32 |
+
"_last_obs": {
|
| 33 |
+
":type:": "<class 'numpy.ndarray'>",
|
| 34 |
+
":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAAHM6GD4Lkw0/hpIyPcS90L6UzJM9Zg6+vAAAAAAAAAAAWj23vcEtWD7zzP88OptOvtZKiLxdPgy8AAAAAAAAAABG8z6+ASGSvNL6AjtV9z85r8wAPtSCJ7oAAIA/AACAP0B+uL326CC4e8QuuDG6PrNPtZ+3G/hONwAAgD8AAAAA4KExvqxDtDyo7EU+dBnrvHiTPL6Kwvk9AACAPwAAgD+a+fg7CgOtP9/PAz4J1Q+/hYFJO6CDXz0AAAAAAAAAACDliD6MZL8+e1c7vZpFsL7JMZQ9MJlSvQAAAAAAAAAA2iGlvfYkObpN+OC6MV8itlkJQjscvwM6AACAPwAAAABggys+yDyIvJ50w7oNBQk5EOHyvXg2AzoAAIA/AACAP1rqMj5Nd6k/adADP2/y8751LY8+9hFqPgAAAAAAAAAAUwUkPogFGj9Y51M9TYLSvmc/3D3ad828AAAAAAAAAABmOtq97zlCPbDTtT1CS0K+oEhBvP0tKzsAAAAAAAAAAGacgD0Lv6g//HeLPs+38b6csaU9Hg5uPQAAAAAAAAAAoJ4tvt/VLT8AT0m++7kVvzG+Fb6lx3k9AAAAAAAAAABmJ10+KVeQPhk9M75vcLG+DaVIPYbmMb4AAAAAAAAAAKZFMD4ooai8UTuSuBLskjctUhe+ol3dNwAAgD8AAIA/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksQSwiGlIwBQ5R0lFKULg=="
|
| 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": -0.015808000000000044,
|
| 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": 310,
|
| 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:": "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"
|
| 98 |
+
}
|
| 99 |
+
}
|
ppo-LunarLander-v2/policy.optimizer.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:2dad9c4c489cad9da472813f824ba413e9a6459fbae3419b1e69549d27cde302
|
| 3 |
+
size 88362
|
ppo-LunarLander-v2/policy.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:65d6f4997ffc86d28d09f79a1739135706b77d16860be015ce75d2ffb74261e1
|
| 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:a4dac715cfef8e9bc3249c6adf8235494bc3379370485f118ee4b9bf9dcc3959
|
| 3 |
+
size 186946
|
results.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"mean_reward": 257.0159493, "std_reward": 17.293941646009166, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2025-02-10T23:47:11.744514"}
|