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 +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: 255.65 +/- 14.64
|
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 0x14002c0d0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x14002c160>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x14002c1f0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x14002c280>", "_build": "<function ActorCriticPolicy._build at 0x14002c310>", "forward": "<function ActorCriticPolicy.forward at 0x14002c3a0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x14002c430>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x14002c4c0>", "_predict": "<function ActorCriticPolicy._predict at 0x14002c550>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x14002c5e0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x14002c670>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x14002c700>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x14001ef80>"}, "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:": "gAWViAAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////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": 1673380451521121000, "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, "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:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "macOS-12.1-arm64-i386-64bit Darwin Kernel Version 21.2.0: Sun Nov 28 20:28:41 PST 2021; root:xnu-8019.61.5~1/RELEASE_ARM64_T6000", "Python": "3.10.0", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1", "GPU Enabled": "False", "Numpy": "1.24.1", "Gym": "0.21.0"}}
|
ppo-LunarLander-v2.zip
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:51671c27a35ae2b393b588d5121da56da4dec9a582f9439ba12fd0851baba692
|
3 |
+
size 147075
|
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 0x14002c0d0>",
|
8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x14002c160>",
|
9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x14002c1f0>",
|
10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x14002c280>",
|
11 |
+
"_build": "<function ActorCriticPolicy._build at 0x14002c310>",
|
12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x14002c3a0>",
|
13 |
+
"extract_features": "<function ActorCriticPolicy.extract_features at 0x14002c430>",
|
14 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x14002c4c0>",
|
15 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x14002c550>",
|
16 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x14002c5e0>",
|
17 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x14002c670>",
|
18 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x14002c700>",
|
19 |
+
"__abstractmethods__": "frozenset()",
|
20 |
+
"_abc_impl": "<_abc._abc_data object at 0x14001ef80>"
|
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:": "gAWViAAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////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": 1673380451521121000,
|
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:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAAIBhTz32xG66E009uGYTO7NxrlC6Ko1dNwAAgD8AAIA/zWssvVzLc7qiuU43r8w7MjriPDoLCXK2AACAPwAAgD8A4yo97FmNufauvTpVlV02qBFbO4YY47kAAIA/AACAPwAi0D0pTBy6Vq+bNtqSFjLsL3w7pkq6tQAAgD8AAIA/ZhbVulIo8bkeiQm3SUNHsoejuTt2+SQ2AACAPwAAgD+aywm+XB2hP3oJJb+d6ZK+8tYTvpKTor4AAAAAAAAAAE14cL32bEK6nQbvNg+GpjHsJ0M57f4MtgAAgD8AAIA/gDAQPa6lhrobDFw76fkftVc3m7nJ4ie0AACAPwAAgD8A+Co8w7VkupqQSrkVu0y03E4AO8LKbTgAAIA/AACAP5qLcLxc+1W6bveGOKbcoLU6skc5ipedtwAAgD8AAIA/gELIvURUGj8m2Vs9pQ6SvoUo1r26aBE+AAAAAAAAAAAAKMQ7j+ZQuncktDsdmNA3cl+MO/jKVTYAAIA/AACAP2b5UL0poE+6jYnourVu0bVpq4O4CFAEOgAAgD8AAIA/Zp/RPMMhDrp6Y6c7OgInOIBGsjoG8y23AACAPwAAgD+z5B+9w8F8uv8QOLsAKQg1foasOhouVjoAAIA/AACAPzPEoDxbXQE/ZnjkvXKGQ76OgrE8dPQEvgAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////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:": "gAWVdxAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIchqiCn9LW0CUhpRSlIwBbJRN6AOMAXSUR0Cgv1UG/vfCdX2UKGgGaAloD0MIMnVXdsFeYECUhpRSlGgVTegDaBZHQKDEHsXzlLh1fZQoaAZoCWgPQwjpYWh18lRmQJSGlFKUaBVN6ANoFkdAoMXI22oegnV9lChoBmgJaA9DCKTi/44oEmRAlIaUUpRoFU3oA2gWR0Cgx17KzRhMdX2UKGgGaAloD0MIxr5k40EoZUCUhpRSlGgVTegDaBZHQKDIOINVinZ1fZQoaAZoCWgPQwhY5NcPsQFiQJSGlFKUaBVN6ANoFkdAoMhJwGW2PXV9lChoBmgJaA9DCHufqkID7mZAlIaUUpRoFU3oA2gWR0Cgy5rVWjoIdX2UKGgGaAloD0MI5lsf1pstYUCUhpRSlGgVTegDaBZHQKDL7yPuG9J1fZQoaAZoCWgPQwhBR6ta0tpkQJSGlFKUaBVN6ANoFkdAoMyz6WPcSHV9lChoBmgJaA9DCF8ktOXcIWJAlIaUUpRoFU3oA2gWR0CgzWP6CUX6dX2UKGgGaAloD0MI3NrC81KzZECUhpRSlGgVTegDaBZHQKDPsX9itq51fZQoaAZoCWgPQwhiEFg5tIdaQJSGlFKUaBVN6ANoFkdAoNBiZ6Uqx3V9lChoBmgJaA9DCArZeRsbGmJAlIaUUpRoFU3oA2gWR0Cg0MzYukDZdX2UKGgGaAloD0MIM1GE1O0MZECUhpRSlGgVTegDaBZHQKDRayJKraN1fZQoaAZoCWgPQwgep+hILgFcQJSGlFKUaBVN6ANoFkdAoNFuz2OAAnV9lChoBmgJaA9DCLCSj92Fm2RAlIaUUpRoFU3oA2gWR0Cg0ZD0UXYUdX2UKGgGaAloD0MIwOldvB/uY0CUhpRSlGgVTegDaBZHQKDVvl7tzCF1fZQoaAZoCWgPQwhXIlD9g9NlQJSGlFKUaBVN6ANoFkdAoNpx3iaRZHV9lChoBmgJaA9DCJ87wf7rqmNAlIaUUpRoFU3oA2gWR0Cg2/Z7ojfOdX2UKGgGaAloD0MIOuenOA66YUCUhpRSlGgVTegDaBZHQKDddqzqrzZ1fZQoaAZoCWgPQwi0sKcdfnthQJSGlFKUaBVN6ANoFkdAoN46oGY8dXV9lChoBmgJaA9DCDnSGRj5O2NAlIaUUpRoFU3oA2gWR0Cg3kvT5O8DdX2UKGgGaAloD0MI+1dWmhQLZUCUhpRSlGgVTegDaBZHQKDhdnCfpUx1fZQoaAZoCWgPQwjmeAWiJ9pgQJSGlFKUaBVN6ANoFkdAoOG/jXFtK3V9lChoBmgJaA9DCAYQPpRoA2BAlIaUUpRoFU3oA2gWR0Cg4nIjGDL9dX2UKGgGaAloD0MIoOHNGjxsZECUhpRSlGgVTegDaBZHQKDjFh4MWoF1fZQoaAZoCWgPQwi2SUVjbdlhQJSGlFKUaBVN6ANoFkdAoOVp2+wkgXV9lChoBmgJaA9DCEdX6e46aU1AlIaUUpRoFUv1aBZHQKDl/YGMXJp1fZQoaAZoCWgPQwh5PZgUHwplQJSGlFKUaBVN6ANoFkdAoOYkvK2a2HV9lChoBmgJaA9DCMoV3uWiZGFAlIaUUpRoFU3oA2gWR0Cg5o80UGmldX2UKGgGaAloD0MIWdsUjwt5ZUCUhpRSlGgVTegDaBZHQKDnLxIatLd1fZQoaAZoCWgPQwjRB8vY0ABfQJSGlFKUaBVN6ANoFkdAoOcysMiKSHV9lChoBmgJaA9DCIDTu3i/KmFAlIaUUpRoFU3oA2gWR0Cg51bIDHOsdX2UKGgGaAloD0MIogip21kAYkCUhpRSlGgVTegDaBZHQKDrt2TPjXF1fZQoaAZoCWgPQwgHexNDcmBDQJSGlFKUaBVNJgFoFkdAoOzo1rIo3XV9lChoBmgJaA9DCHB4QURq9V9AlIaUUpRoFU3oA2gWR0Cg8L3Hq/ucdX2UKGgGaAloD0MIfCsSE1RDYkCUhpRSlGgVTegDaBZHQKDyZTBqKxd1fZQoaAZoCWgPQwj7XdiaLdhkQJSGlFKUaBVN6ANoFkdAoPPyR6nivXV9lChoBmgJaA9DCC/cuTDSNGJAlIaUUpRoFU3oA2gWR0Cg9MpN9H+ZdX2UKGgGaAloD0MI02ndBrWFYkCUhpRSlGgVTegDaBZHQKD027nPmgd1fZQoaAZoCWgPQwhaEqCmFgVkQJSGlFKUaBVN6ANoFkdAoPYzmuDBdnV9lChoBmgJaA9DCHKkMzDy5WNAlIaUUpRoFU3oA2gWR0Cg+STgdfb9dX2UKGgGaAloD0MIUfaWcr76YECUhpRSlGgVTegDaBZHQKD5zc0Ltu11fZQoaAZoCWgPQwgAcy1agKFRQJSGlFKUaBVL5GgWR0Cg+ntelbeNdX2UKGgGaAloD0MIPX0E/nCHYkCUhpRSlGgVTegDaBZHQKD7/+Jgssh1fZQoaAZoCWgPQwiCHJQw0xFfQJSGlFKUaBVN6ANoFkdAoPx9m4Ajp3V9lChoBmgJaA9DCDvD1Ja6wmFAlIaUUpRoFU3oA2gWR0Cg/JwPqcEvdX2UKGgGaAloD0MIYwys4/grY0CUhpRSlGgVTegDaBZHQKD89D/lyR11fZQoaAZoCWgPQwiAYmTJHKpeQJSGlFKUaBVN6ANoFkdAoP11JSR8t3V9lChoBmgJaA9DCPIJ2Xmb12BAlIaUUpRoFU3oA2gWR0Cg/ZRBNVR2dX2UKGgGaAloD0MIUHEceLX/YUCUhpRSlGgVTegDaBZHQKEBxKdxyXF1fZQoaAZoCWgPQwj5ZTBGpFxnQJSGlFKUaBVN6ANoFkdAoQLt0zTF2nV9lChoBmgJaA9DCEYotoKmxm9AlIaUUpRoFU2wAWgWR0ChBL8xj8UFdX2UKGgGaAloD0MIXtpwWBr0ZkCUhpRSlGgVTegDaBZHQKEGcpQ1rIp1fZQoaAZoCWgPQwhwsaIG04xFQJSGlFKUaBVL02gWR0ChB2VWjoIOdX2UKGgGaAloD0MINZawNsZTY0CUhpRSlGgVTegDaBZHQKEH4uXeFcp1fZQoaAZoCWgPQwj1oKAUrYFgQJSGlFKUaBVN6ANoFkdAoQn5NKyv93V9lChoBmgJaA9DCEURUrezsWRAlIaUUpRoFU3oA2gWR0ChCgiaJAMVdX2UKGgGaAloD0MIMGghAaMQXkCUhpRSlGgVTegDaBZHQKELVI8QqZt1fZQoaAZoCWgPQwjP2QJC6zRjQJSGlFKUaBVN6ANoFkdAoQ5CX+l0o3V9lChoBmgJaA9DCLmnqzuW12FAlIaUUpRoFU3oA2gWR0ChDu6FVT73dX2UKGgGaAloD0MItmeWBKj9ZkCUhpRSlGgVTegDaBZHQKEPrbA1vVF1fZQoaAZoCWgPQwjvx+2XT4YiQJSGlFKUaBVL92gWR0ChEMPTodMkdX2UKGgGaAloD0MI7DTSUnlhYUCUhpRSlGgVTegDaBZHQKERQ6fapP11fZQoaAZoCWgPQwgHliNkIGtGQJSGlFKUaBVL02gWR0ChEXI8IRh+dX2UKGgGaAloD0MIT3l0IyxJZECUhpRSlGgVTegDaBZHQKERwO1fE4x1fZQoaAZoCWgPQwgFUmLXdgZjQJSGlFKUaBVN6ANoFkdAoRI0xoIv8XV9lChoBmgJaA9DCKDctu9RH0xAlIaUUpRoFUvsaBZHQKESfqZc9nt1fZQoaAZoCWgPQwhPXI5XoBRkQJSGlFKUaBVN6ANoFkdAoRK81n/T9nV9lChoBmgJaA9DCLyUumQcX1lAlIaUUpRoFU3oA2gWR0ChEtyP+4smdX2UKGgGaAloD0MI1JrmHWd/ckCUhpRSlGgVTVQCaBZHQKEUJ2ki2Ul1fZQoaAZoCWgPQwi/mgMEc3Tzv5SGlFKUaBVL6WgWR0ChFM31rZandX2UKGgGaAloD0MIi6n0E84WcUCUhpRSlGgVTYUBaBZHQKEWApmVZ9x1fZQoaAZoCWgPQwjgoL36eBROQJSGlFKUaBVL62gWR0ChFheoDPnkdX2UKGgGaAloD0MIya1JtyUVYUCUhpRSlGgVTegDaBZHQKEWcNzbN8p1fZQoaAZoCWgPQwjVzjC1pfZNQJSGlFKUaBVL6WgWR0ChFnXOv+wUdX2UKGgGaAloD0MIXOUJhB0aYECUhpRSlGgVTegDaBZHQKEYx+ee4Cp1fZQoaAZoCWgPQwieJF0zeSxiQJSGlFKUaBVN6ANoFkdAoRpX27FsHnV9lChoBmgJaA9DCDQsRl1rpV5AlIaUUpRoFU3oA2gWR0ChG68QAdXDdX2UKGgGaAloD0MIH2lwW1tPY0CUhpRSlGgVTegDaBZHQKEduulGgBd1fZQoaAZoCWgPQwj3dktygClxQJSGlFKUaBVNvAJoFkdAoR47q0MPSXV9lChoBmgJaA9DCGVR2EXREHBAlIaUUpRoFU37AmgWR0ChIrYjKPn0dX2UKGgGaAloD0MITMKFPIK8X0CUhpRSlGgVTegDaBZHQKEjG4aP0Zp1fZQoaAZoCWgPQwjCo40j1gIwQJSGlFKUaBVNAQFoFkdAoSMd89fTkXV9lChoBmgJaA9DCHUDBd7Jh2NAlIaUUpRoFU3oA2gWR0ChJJ1u76HkdX2UKGgGaAloD0MIYKxvYPI9ZECUhpRSlGgVTegDaBZHQKElL8WsRxt1fZQoaAZoCWgPQwhKCiyAKUZjQJSGlFKUaBVN6ANoFkdAoSVycG1QZXV9lChoBmgJaA9DCATo9/0bh29AlIaUUpRoFU14AmgWR0ChJ+znJT2ndX2UKGgGaAloD0MIVtKKb6hpZECUhpRSlGgVTegDaBZHQKEoIfyPMjh1fZQoaAZoCWgPQwjgoL36+OBgQJSGlFKUaBVN6ANoFkdAoSjrl3hXKnV9lChoBmgJaA9DCNBDbRtGkmVAlIaUUpRoFU3oA2gWR0ChKkqO1fE5dX2UKGgGaAloD0MIjgWFQZk+X0CUhpRSlGgVTegDaBZHQKEqYkfLcKx1fZQoaAZoCWgPQwhu3c1TnWphQJSGlFKUaBVN6ANoFkdAoSq9+mWMTHV9lChoBmgJaA9DCD9W8NsQtV9AlIaUUpRoFU3oA2gWR0ChKsNk4FRpdX2UKGgGaAloD0MIKPG5E+y+Y0CUhpRSlGgVTegDaBZHQKEtA58Sf191fZQoaAZoCWgPQwjCbW3huWpwQJSGlFKUaBVNEwJoFkdAoS5TEUCaJHV9lChoBmgJaA9DCG8O12oP2yRAlIaUUpRoFUvlaBZHQKEuqgkC3gF1fZQoaAZoCWgPQwgtX5fhvxdjQJSGlFKUaBVN6ANoFkdAoTHbYf4h2XV9lChoBmgJaA9DCOz4LxAEv2FAlIaUUpRoFU3oA2gWR0ChMmIUzsQedX2UKGgGaAloD0MIumbyzbbRY0CUhpRSlGgVTegDaBZHQKE0P89fTkR1ZS4="
|
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-LunarLander-v2/policy.optimizer.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:1d4000414bb8236ff1a4e04e8d1aa3f9cfeb2cd2c9cf219825e34cbc17fb450a
|
3 |
+
size 87545
|
ppo-LunarLander-v2/policy.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:fa2c59f63c3434d6ca109e2c4280aa84b64edf812429b261c8e584ea3a0e4eb8
|
3 |
+
size 43265
|
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: macOS-12.1-arm64-i386-64bit Darwin Kernel Version 21.2.0: Sun Nov 28 20:28:41 PST 2021; root:xnu-8019.61.5~1/RELEASE_ARM64_T6000
|
2 |
+
- Python: 3.10.0
|
3 |
+
- Stable-Baselines3: 1.7.0
|
4 |
+
- PyTorch: 1.13.1
|
5 |
+
- GPU Enabled: False
|
6 |
+
- Numpy: 1.24.1
|
7 |
+
- Gym: 0.21.0
|
replay.mp4
ADDED
Binary file (405 kB). View file
|
|
results.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"mean_reward": 255.65101916299022, "std_reward": 14.64124454832991, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-10T21:32:06.069848"}
|