Push LunarLander-v2 model
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 +96 -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: 264.70 +/- 23.24
|
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 0x7fc011a00ca0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fc011a00d30>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fc011a00dc0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fc011a00e50>", "_build": "<function ActorCriticPolicy._build at 0x7fc011a00ee0>", "forward": "<function ActorCriticPolicy.forward at 0x7fc011a00f70>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fc011a01000>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fc011a01090>", "_predict": "<function ActorCriticPolicy._predict at 0x7fc011a01120>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fc011a011b0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fc011a01240>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fc011a012d0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fc0119f70c0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1682893167370129574, "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": -0.015808000000000044, "_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": 276, "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, "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-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.10.11", "Stable-Baselines3": "1.8.0", "PyTorch": "2.0.0+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}
|
ppo-LunarLander-v2.zip
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:75a0e155a967a053b9c5b6d6f81b8f9c1614adb7deca5b7b820d2ce5679b549b
|
3 |
+
size 147392
|
ppo-LunarLander-v2/_stable_baselines3_version
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
1.8.0
|
ppo-LunarLander-v2/data
ADDED
@@ -0,0 +1,96 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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 0x7fc011a00ca0>",
|
8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fc011a00d30>",
|
9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fc011a00dc0>",
|
10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fc011a00e50>",
|
11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7fc011a00ee0>",
|
12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7fc011a00f70>",
|
13 |
+
"extract_features": "<function ActorCriticPolicy.extract_features at 0x7fc011a01000>",
|
14 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fc011a01090>",
|
15 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7fc011a01120>",
|
16 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fc011a011b0>",
|
17 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fc011a01240>",
|
18 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7fc011a012d0>",
|
19 |
+
"__abstractmethods__": "frozenset()",
|
20 |
+
"_abc_impl": "<_abc._abc_data object at 0x7fc0119f70c0>"
|
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": 1682893167370129574,
|
30 |
+
"learning_rate": 0.0003,
|
31 |
+
"tensorboard_log": null,
|
32 |
+
"lr_schedule": {
|
33 |
+
":type:": "<class 'function'>",
|
34 |
+
":serialized:": "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"
|
35 |
+
},
|
36 |
+
"_last_obs": {
|
37 |
+
":type:": "<class 'numpy.ndarray'>",
|
38 |
+
":serialized:": "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"
|
39 |
+
},
|
40 |
+
"_last_episode_starts": {
|
41 |
+
":type:": "<class 'numpy.ndarray'>",
|
42 |
+
":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="
|
43 |
+
},
|
44 |
+
"_last_original_obs": null,
|
45 |
+
"_episode_num": 0,
|
46 |
+
"use_sde": false,
|
47 |
+
"sde_sample_freq": -1,
|
48 |
+
"_current_progress_remaining": -0.015808000000000044,
|
49 |
+
"_stats_window_size": 100,
|
50 |
+
"ep_info_buffer": {
|
51 |
+
":type:": "<class 'collections.deque'>",
|
52 |
+
":serialized:": "gAWVfhAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIu7VMhmNHZ0CUhpRSlIwBbJRN6AOMAXSUR0CZe9XMyJsPdX2UKGgGaAloD0MI6Xx4liB5WUCUhpRSlGgVTegDaBZHQJmAxWNm16V1fZQoaAZoCWgPQwjTTWIQWGNfQJSGlFKUaBVN6ANoFkdAmYfHEdeY2XV9lChoBmgJaA9DCKvP1VbskmBAlIaUUpRoFU3oA2gWR0CZh+67NB4VdX2UKGgGaAloD0MIXfqXpLJ8YECUhpRSlGgVTegDaBZHQJmJGKsMiKR1fZQoaAZoCWgPQwhsBOJ1fV9lQJSGlFKUaBVN6ANoFkdAmY+Cz1K5CnV9lChoBmgJaA9DCHP2zmgr6mJAlIaUUpRoFU3oA2gWR0CZlU/KyOaOdX2UKGgGaAloD0MIDykGSDRuY0CUhpRSlGgVTegDaBZHQJmhcfRu0kZ1fZQoaAZoCWgPQwgYPiKmRC1bQJSGlFKUaBVN6ANoFkdAmaSvX9R77nV9lChoBmgJaA9DCAzqW+Z0zWBAlIaUUpRoFU3oA2gWR0CZqczByjpLdX2UKGgGaAloD0MInl2+9WGFY0CUhpRSlGgVTegDaBZHQJmwjgaWHDd1fZQoaAZoCWgPQwjmWN5VD2xiQJSGlFKUaBVN6ANoFkdAmbED0Yj0MHV9lChoBmgJaA9DCPWidr8KYGNAlIaUUpRoFU3oA2gWR0CZsviO/+KkdX2UKGgGaAloD0MII59XPPU8Q0CUhpRSlGgVS/xoFkdAmcZkbo8p1HV9lChoBmgJaA9DCNoCQuvhgGdAlIaUUpRoFU3oA2gWR0CZySuMdcSodX2UKGgGaAloD0MImFEstzSVYUCUhpRSlGgVTegDaBZHQJnqAjRlYlp1fZQoaAZoCWgPQwgYeVkTi2RlQJSGlFKUaBVN6ANoFkdAmes8rZrYXnV9lChoBmgJaA9DCNCX3v7c0mVAlIaUUpRoFU3oA2gWR0CZ62GSIP9UdX2UKGgGaAloD0MIveXqx6ZLZ0CUhpRSlGgVTegDaBZHQJnwQcjqv/11fZQoaAZoCWgPQwiIE5hOK39xQJSGlFKUaBVN4QFoFkdAmfQHq/ub7XV9lChoBmgJaA9DCL+5v3rcU0xAlIaUUpRoFUvtaBZHQJn1Hfk3juN1fZQoaAZoCWgPQwi/02TGWxlkQJSGlFKUaBVN6ANoFkdAmfb9jbzshXV9lChoBmgJaA9DCAiqRq8Gsl9AlIaUUpRoFU3oA2gWR0CZ9yLh73PBdX2UKGgGaAloD0MI+3YSEf5HZUCUhpRSlGgVTegDaBZHQJn4KowVTJh1fZQoaAZoCWgPQwikG2FRER5nQJSGlFKUaBVN6ANoFkdAmf3EcKgIyHV9lChoBmgJaA9DCAgAjj37E2BAlIaUUpRoFU3oA2gWR0CaAk7cfvF4dX2UKGgGaAloD0MIlZ9U+/QAYECUhpRSlGgVTegDaBZHQJoJsoVmBe51fZQoaAZoCWgPQwgYsU8AxQxeQJSGlFKUaBVN6ANoFkdAmguwSeyzHHV9lChoBmgJaA9DCNzY7Eh18mNAlIaUUpRoFU3oA2gWR0CaDxJkoWpIdX2UKGgGaAloD0MIM1AZ/z4bSUCUhpRSlGgVS/RoFkdAmhTpof0VanV9lChoBmgJaA9DCIFB0qdVgWdAlIaUUpRoFU3oA2gWR0CaFlnr6ciGdX2UKGgGaAloD0MIpcACmDJfY0CUhpRSlGgVTegDaBZHQJosuUaAFxJ1fZQoaAZoCWgPQwieCrjneR1iQJSGlFKUaBVN6ANoFkdAmjXfDxb0OHV9lChoBmgJaA9DCBmQvd794WJAlIaUUpRoFU3oA2gWR0CaTETqjaf0dX2UKGgGaAloD0MI6UfDKfN0ZUCUhpRSlGgVTegDaBZHQJpMd0OmR/51fZQoaAZoCWgPQwiG5c+3BSVnQJSGlFKUaBVN6ANoFkdAmlNlA7gbZXV9lChoBmgJaA9DCD3S4La2QGZAlIaUUpRoFU3oA2gWR0CaWRvxH5JsdX2UKGgGaAloD0MIkdPX8zVDZ0CUhpRSlGgVTegDaBZHQJpau6RQrMF1fZQoaAZoCWgPQwiPVUrP9M5gQJSGlFKUaBVN6ANoFkdAml2YZl4C63V9lChoBmgJaA9DCN1FmKJcXV5AlIaUUpRoFU3oA2gWR0CaXcvmozeodX2UKGgGaAloD0MIhBCQLyHMZkCUhpRSlGgVTegDaBZHQJpfPriVB2R1fZQoaAZoCWgPQwhV3/lFicliQJSGlFKUaBVN6ANoFkdAmmU53C9AX3V9lChoBmgJaA9DCMJrlzYcR2BAlIaUUpRoFU3oA2gWR0CacbqslsxgdX2UKGgGaAloD0MIIjmZuFW3YkCUhpRSlGgVTegDaBZHQJpz5UaQ3gl1fZQoaAZoCWgPQwiu1onL8YJiQJSGlFKUaBVN6ANoFkdAmnchQFcIJXV9lChoBmgJaA9DCFd3LLZJGWJAlIaUUpRoFU3oA2gWR0Cae2okRjBmdX2UKGgGaAloD0MIO+XRjbAGZECUhpRSlGgVTegDaBZHQJp8ZyMkyDZ1fZQoaAZoCWgPQwgmcOtunhliQJSGlFKUaBVN6ANoFkdAmpCIDklu33V9lChoBmgJaA9DCM5Q3PGmsmFAlIaUUpRoFU3oA2gWR0CanUyq+8GtdX2UKGgGaAloD0MIuoRDb/FYXkCUhpRSlGgVTegDaBZHQJqekYO2AoZ1fZQoaAZoCWgPQwi+v0F79bdkQJSGlFKUaBVN6ANoFkdAmp64o/iYLXV9lChoBmgJaA9DCKxxNh2BGGJAlIaUUpRoFU3oA2gWR0CauNDYh+vydX2UKGgGaAloD0MI2zAKgsdVX0CUhpRSlGgVTegDaBZHQJq8tvgm7at1fZQoaAZoCWgPQwiDwwsiUsFgQJSGlFKUaBVN6ANoFkdAmr3dwBHTZ3V9lChoBmgJaA9DCGIx6lr7UGhAlIaUUpRoFU3oA2gWR0Cav7X8O09hdX2UKGgGaAloD0MInE1HALfcZUCUhpRSlGgVTegDaBZHQJq/2ZVn27F1fZQoaAZoCWgPQwg8LxUb841iQJSGlFKUaBVN6ANoFkdAmsDU9QoCuHV9lChoBmgJaA9DCP7zNGCQ2mZAlIaUUpRoFU3oA2gWR0CaxwoH9m6HdX2UKGgGaAloD0MIZ0eq73ycZUCUhpRSlGgVTegDaBZHQJrYbiIcinp1fZQoaAZoCWgPQwhMOPQWD85tQJSGlFKUaBVNzgNoFkdAmti8jeKsMnV9lChoBmgJaA9DCCWRfZBlMF9AlIaUUpRoFU3oA2gWR0Ca3gGIbfgrdX2UKGgGaAloD0MI98jmqnnXYkCUhpRSlGgVTegDaBZHQJriKMm4RVZ1fZQoaAZoCWgPQwhyMQbWcVRoQJSGlFKUaBVN6ANoFkdAmuMcHv+fiHV9lChoBmgJaA9DCBVSflJtiHBAlIaUUpRoFU20AmgWR0Ca54bKzRhMdX2UKGgGaAloD0MIMPXzpiI0ZECUhpRSlGgVTegDaBZHQJr0Kt8uzyB1fZQoaAZoCWgPQwg6HjNQGfthQJSGlFKUaBVN6ANoFkdAmvzPfoA4oHV9lChoBmgJaA9DCNP4hVcSFWVAlIaUUpRoFU3oA2gWR0Ca/jVMEidKdX2UKGgGaAloD0MICRfyCG77YkCUhpRSlGgVTegDaBZHQJseztTkyUN1fZQoaAZoCWgPQwheLuI7MYVnQJSGlFKUaBVN6ANoFkdAmyLQ+hXbNHV9lChoBmgJaA9DCCeJJeXuYWJAlIaUUpRoFU3oA2gWR0CbJAdtVJcxdX2UKGgGaAloD0MI9nmM8kyEY0CUhpRSlGgVTegDaBZHQJsmEvEjxCp1fZQoaAZoCWgPQwjT2F4Lej5kQJSGlFKUaBVN6ANoFkdAmyY3+dbxE3V9lChoBmgJaA9DCG1vtyQHtGBAlIaUUpRoFU3oA2gWR0CbJ0+lj3EidX2UKGgGaAloD0MIaF2j5cArZECUhpRSlGgVTegDaBZHQJss1YRujyp1fZQoaAZoCWgPQwghyEEJsxxlQJSGlFKUaBVN6ANoFkdAmzjz3RG+bnV9lChoBmgJaA9DCEioGVJFwWRAlIaUUpRoFU3oA2gWR0CbOS0OEug6dX2UKGgGaAloD0MI/KawUsFjY0CUhpRSlGgVTegDaBZHQJs94/yGzrx1fZQoaAZoCWgPQwinAu55foZkQJSGlFKUaBVN6ANoFkdAm0MJzT4L1HV9lChoBmgJaA9DCKeWrfVFuWdAlIaUUpRoFU3oA2gWR0CbREJQLux9dX2UKGgGaAloD0MI68VQTrTnZUCUhpRSlGgVTegDaBZHQJtKNTefqX51fZQoaAZoCWgPQwjS4oxhznJhQJSGlFKUaBVN6ANoFkdAm1mluWKMvXV9lChoBmgJaA9DCDeLFwtDQmJAlIaUUpRoFU3oA2gWR0CbYdfeUILPdX2UKGgGaAloD0MI0LUvoBeoZUCUhpRSlGgVTegDaBZHQJtjLT8YQ8R1fZQoaAZoCWgPQwhEv7Z++nheQJSGlFKUaBVN6ANoFkdAm32DX4CZGHV9lChoBmgJaA9DCCkhWFWv+2FAlIaUUpRoFU3oA2gWR0Cbgrmce8wpdX2UKGgGaAloD0MIcXFUbqKuZUCUhpRSlGgVTegDaBZHQJuER2HLzPN1fZQoaAZoCWgPQwijzXFuE6lgQJSGlFKUaBVN6ANoFkdAm4bWvwEyL3V9lChoBmgJaA9DCMCxZ89lnmZAlIaUUpRoFU3oA2gWR0CbhwY1He7+dX2UKGgGaAloD0MIlDKpoQ0fW0CUhpRSlGgVTegDaBZHQJuIep97Wup1fZQoaAZoCWgPQwgJOIQqNXhlQJSGlFKUaBVN6ANoFkdAm5B3QhOgx3V9lChoBmgJaA9DCOaw+45hc2BAlIaUUpRoFU3oA2gWR0CbnUN6gM+edX2UKGgGaAloD0MI7GexFMkfYUCUhpRSlGgVTegDaBZHQJudfjOs1bd1fZQoaAZoCWgPQwjcvHFSmENaQJSGlFKUaBVN6ANoFkdAm6LC/47A+XV9lChoBmgJaA9DCD5A9+XM7GFAlIaUUpRoFU3oA2gWR0Cbp0UQ04zadX2UKGgGaAloD0MIkuf6PpwpYECUhpRSlGgVTegDaBZHQJuoTK+zt1J1fZQoaAZoCWgPQwjP9ugNdxpiQJSGlFKUaBVN6ANoFkdAm6zIGY8dP3V9lChoBmgJaA9DCMVYpl8i7WZAlIaUUpRoFU3oA2gWR0CbuXZTyauwdX2UKGgGaAloD0MIBfnZyPUYY0CUhpRSlGgVTegDaBZHQJvEhxAB1cN1fZQoaAZoCWgPQwirsYS1MSpiQJSGlFKUaBVN6ANoFkdAm8ZaVt4zJ3V9lChoBmgJaA9DCKFJYkk5r2ZAlIaUUpRoFU3oA2gWR0CbzTTcIqsmdWUu"
|
53 |
+
},
|
54 |
+
"ep_success_buffer": {
|
55 |
+
":type:": "<class 'collections.deque'>",
|
56 |
+
":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
|
57 |
+
},
|
58 |
+
"_n_updates": 276,
|
59 |
+
"observation_space": {
|
60 |
+
":type:": "<class 'gym.spaces.box.Box'>",
|
61 |
+
":serialized:": "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",
|
62 |
+
"dtype": "float32",
|
63 |
+
"_shape": [
|
64 |
+
8
|
65 |
+
],
|
66 |
+
"low": "[-inf -inf -inf -inf -inf -inf -inf -inf]",
|
67 |
+
"high": "[inf inf inf inf inf inf inf inf]",
|
68 |
+
"bounded_below": "[False False False False False False False False]",
|
69 |
+
"bounded_above": "[False False False False False False False False]",
|
70 |
+
"_np_random": null
|
71 |
+
},
|
72 |
+
"action_space": {
|
73 |
+
":type:": "<class 'gym.spaces.discrete.Discrete'>",
|
74 |
+
":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
|
75 |
+
"n": 4,
|
76 |
+
"_shape": [],
|
77 |
+
"dtype": "int64",
|
78 |
+
"_np_random": null
|
79 |
+
},
|
80 |
+
"n_envs": 16,
|
81 |
+
"n_steps": 1024,
|
82 |
+
"gamma": 0.999,
|
83 |
+
"gae_lambda": 0.98,
|
84 |
+
"ent_coef": 0.01,
|
85 |
+
"vf_coef": 0.5,
|
86 |
+
"max_grad_norm": 0.5,
|
87 |
+
"batch_size": 64,
|
88 |
+
"n_epochs": 4,
|
89 |
+
"clip_range": {
|
90 |
+
":type:": "<class 'function'>",
|
91 |
+
":serialized:": "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"
|
92 |
+
},
|
93 |
+
"clip_range_vf": null,
|
94 |
+
"normalize_advantage": true,
|
95 |
+
"target_kl": null
|
96 |
+
}
|
ppo-LunarLander-v2/policy.optimizer.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:ba94a26bcfe76b54e27b7179168592576885eb422f0444b13fc3a154ccadba21
|
3 |
+
size 87929
|
ppo-LunarLander-v2/policy.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:431bd2f3fee71594949d2457ecce5d429ca55bf670272208fa3fff97b9cbd133
|
3 |
+
size 43329
|
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-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
|
2 |
+
- Python: 3.10.11
|
3 |
+
- Stable-Baselines3: 1.8.0
|
4 |
+
- PyTorch: 2.0.0+cu118
|
5 |
+
- GPU Enabled: True
|
6 |
+
- Numpy: 1.22.4
|
7 |
+
- Gym: 0.21.0
|
replay.mp4
ADDED
Binary file (192 kB). View file
|
|
results.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"mean_reward": 264.69796066824625, "std_reward": 23.238677284576525, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-30T23:46:50.216725"}
|