complete first hands-on using PPO
Browse files- 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 +95 -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 +1,37 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 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: 282.77 +/- 15.37
|
| 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 0x7fd30b6c5820>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fd30b6c58b0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fd30b6c5940>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fd30b6c59d0>", "_build": "<function ActorCriticPolicy._build at 0x7fd30b6c5a60>", "forward": "<function ActorCriticPolicy.forward at 0x7fd30b6c5af0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fd30b6c5b80>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fd30b6c5c10>", "_predict": "<function ActorCriticPolicy._predict at 0x7fd30b6c5ca0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fd30b6c5d30>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fd30b6c5dc0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fd30b6c5e50>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fd30b6c0a20>"}, "verbose": 1, "policy_kwargs": {}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "gAWVnwEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLCIWUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWIAAAAAAAAAAAAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/5RoCksIhZSMAUOUdJRSlIwEaGlnaJRoEiiWIAAAAAAAAAAAAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf5RoCksIhZRoFXSUUpSMDWJvdW5kZWRfYmVsb3eUaBIolggAAAAAAAAAAAAAAAAAAACUaAeMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLCIWUaBV0lFKUjA1ib3VuZGVkX2Fib3ZllGgSKJYIAAAAAAAAAAAAAAAAAAAAlGghSwiFlGgVdJRSlIwKX25wX3JhbmRvbZROdWIu", "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": 2015232, "_total_timesteps": 2000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1674817685577903655, "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": -0.007616000000000067, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 492, "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-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.10", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "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 sha256:0b009e3da6fc154448e9c8053c57bf790293b34fbf20fe155f7feaaa2c08339f
|
| 3 |
+
size 147316
|
ppo-LunarLander-v2/_stable_baselines3_version
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
1.7.0
|
ppo-LunarLander-v2/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 0x7fd30b6c5820>",
|
| 8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fd30b6c58b0>",
|
| 9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fd30b6c5940>",
|
| 10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fd30b6c59d0>",
|
| 11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7fd30b6c5a60>",
|
| 12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7fd30b6c5af0>",
|
| 13 |
+
"extract_features": "<function ActorCriticPolicy.extract_features at 0x7fd30b6c5b80>",
|
| 14 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fd30b6c5c10>",
|
| 15 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7fd30b6c5ca0>",
|
| 16 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fd30b6c5d30>",
|
| 17 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fd30b6c5dc0>",
|
| 18 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7fd30b6c5e50>",
|
| 19 |
+
"__abstractmethods__": "frozenset()",
|
| 20 |
+
"_abc_impl": "<_abc_data object at 0x7fd30b6c0a20>"
|
| 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": 2015232,
|
| 47 |
+
"_total_timesteps": 2000000,
|
| 48 |
+
"_num_timesteps_at_start": 0,
|
| 49 |
+
"seed": null,
|
| 50 |
+
"action_noise": null,
|
| 51 |
+
"start_time": 1674817685577903655,
|
| 52 |
+
"learning_rate": 0.0003,
|
| 53 |
+
"tensorboard_log": null,
|
| 54 |
+
"lr_schedule": {
|
| 55 |
+
":type:": "<class 'function'>",
|
| 56 |
+
":serialized:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/M6kqMFUyYYWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="
|
| 57 |
+
},
|
| 58 |
+
"_last_obs": {
|
| 59 |
+
":type:": "<class 'numpy.ndarray'>",
|
| 60 |
+
":serialized:": "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"
|
| 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": -0.007616000000000067,
|
| 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": 492,
|
| 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-LunarLander-v2/policy.optimizer.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:badf952f70ee224b842605f45d5c6e975277643a4cab7f8eaa7489eac69632e6
|
| 3 |
+
size 87929
|
ppo-LunarLander-v2/policy.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:56ab450ff6ea6f031ad42284f5d07375b9bc58925030db6ad50c2dd44437f34d
|
| 3 |
+
size 43393
|
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-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
|
| 2 |
+
- Python: 3.8.10
|
| 3 |
+
- Stable-Baselines3: 1.7.0
|
| 4 |
+
- PyTorch: 1.13.1+cu116
|
| 5 |
+
- GPU Enabled: True
|
| 6 |
+
- Numpy: 1.21.6
|
| 7 |
+
- Gym: 0.21.0
|
replay.mp4
ADDED
|
Binary file (186 kB). View file
|
|
|
results.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"mean_reward": 282.7710194921698, "std_reward": 15.373826126431245, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-27T12:01:13.616150"}
|