Lakshya2k commited on
Commit
fadf4ae
1 Parent(s): 184e951

Colab-PPO-LunarLander-v2-1e6

Browse files
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: 241.12 +/- 17.38
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 0x7fbc6b330f70>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fbc6b334040>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fbc6b3340d0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fbc6b334160>", "_build": "<function ActorCriticPolicy._build at 0x7fbc6b3341f0>", "forward": "<function ActorCriticPolicy.forward at 0x7fbc6b334280>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fbc6b334310>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fbc6b3343a0>", "_predict": "<function ActorCriticPolicy._predict at 0x7fbc6b334430>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fbc6b3344c0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fbc6b334550>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fbc6b3345e0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fbc6b32ca50>"}, "verbose": 0, "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:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////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": 1677170546493443909, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAAIYiVz4uwrE79uwpvqRSJD26RWo+eQ2IvQAAgD8AAIA/68qVvsMfVbyqRtk5pqY/OMkxwz3GvZC4AACAPwAAAABQ2Zy+zFXiPkDW7z16CGm+RzchvMuFAT0AAAAAAAAAABq55L1WWyY9WskvPmtKHr6rEE09csIvvQAAAAAAAAAAQFY3vrtmkLwh1iQ5WfBqN2eT/D1QPlS4AACAPwAAgD+NfVs+KdILvMpRhTrKNiW439dcvcgBmrkAAIA/AACAP6Y6pz4PSR89F00ivE5gLb7RlLk9xirhPgAAgD8AAAAAiu24PlfxBz84W/69VjhIvt015DuTm0w8AAAAAAAAAACNb2s+fNdUPiog3TxbojO+aOpiPEUBEDwAAAAAAAAAAE0n3j2qjW8/n0SVPQXvs775sjQ9FThRPAAAAAAAAAAAMxSwvcPRKro9mty583yItQVEp7o5LgA5AACAPwAAgD8zhzq+JF9ePIaOALuhQx85tZP5vdLkJDoAAIA/AACAP3O6Az63vFk+tt8BvmrPFL4lPKG8UA9pvAAAAAAAAAAAk9LGPmDhDj87h/o85D6TviZjkD31SHq8AAAAAAAAAABgojC+jjGNvENi/jmUA2k43WwDPi2fLbkAAIA/AACAP7PnB76D50095ppDPl6qdL5EgBk9KvxfPQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksQSwiGlIwBQ5R0lFKULg=="}, "_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": 310, "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, "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": "1.22.4", "Gym": "0.21.0"}}
ppo-lunarlander-1e6.zip ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:d8a936465e8fd27a6df3556a59653e902425ebb2c96fc4905a7ccf8f7dd79c4e
3
+ size 147415
ppo-lunarlander-1e6/_stable_baselines3_version ADDED
@@ -0,0 +1 @@
 
 
1
+ 1.7.0
ppo-lunarlander-1e6/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 0x7fbc6b330f70>",
8
+ "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fbc6b334040>",
9
+ "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fbc6b3340d0>",
10
+ "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fbc6b334160>",
11
+ "_build": "<function ActorCriticPolicy._build at 0x7fbc6b3341f0>",
12
+ "forward": "<function ActorCriticPolicy.forward at 0x7fbc6b334280>",
13
+ "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fbc6b334310>",
14
+ "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fbc6b3343a0>",
15
+ "_predict": "<function ActorCriticPolicy._predict at 0x7fbc6b334430>",
16
+ "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fbc6b3344c0>",
17
+ "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fbc6b334550>",
18
+ "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fbc6b3345e0>",
19
+ "__abstractmethods__": "frozenset()",
20
+ "_abc_impl": "<_abc_data object at 0x7fbc6b32ca50>"
21
+ },
22
+ "verbose": 0,
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": 1015808,
47
+ "_total_timesteps": 1000000,
48
+ "_num_timesteps_at_start": 0,
49
+ "seed": null,
50
+ "action_noise": null,
51
+ "start_time": 1677170546493443909,
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:": "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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": -0.015808000000000044,
71
+ "ep_info_buffer": {
72
+ ":type:": "<class 'collections.deque'>",
73
+ ":serialized:": "gAWVexAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIHsL4adxsbECUhpRSlIwBbJRNGAGMAXSUR0CgddFrl/6PdX2UKGgGaAloD0MIgzEiUWguYkCUhpRSlGgVTegDaBZHQKB17dxhlUZ1fZQoaAZoCWgPQwjAd5s3zn1pQJSGlFKUaBVNHAFoFkdAoHYGXw9aEHV9lChoBmgJaA9DCHRfzmyXTnBAlIaUUpRoFU01AWgWR0CgdkumzjWDdX2UKGgGaAloD0MIIlD9g4gDcECUhpRSlGgVTRYBaBZHQKB3R7jT8YR1fZQoaAZoCWgPQwiwIM1YtPxpQJSGlFKUaBVNGwFoFkdAoHgJfv4M4XV9lChoBmgJaA9DCC2WIvnK3G1AlIaUUpRoFU1BAWgWR0CgeA6xgRbsdX2UKGgGaAloD0MIKcx7nOnoa0CUhpRSlGgVTSkBaBZHQKB4JBF/hEV1fZQoaAZoCWgPQwhZhjjWxZtxQJSGlFKUaBVNlwFoFkdAoHkzRMN+b3V9lChoBmgJaA9DCIqT+x0KaG5AlIaUUpRoFU02AWgWR0CgeeFqJuVHdX2UKGgGaAloD0MI63B0le4JYECUhpRSlGgVTegDaBZHQKB6A1uR9w51fZQoaAZoCWgPQwhklj0J7PhvQJSGlFKUaBVNRAFoFkdAoHqxW3jMmnV9lChoBmgJaA9DCKINwAZEMnBAlIaUUpRoFU0cAWgWR0CgezYZVGTcdX2UKGgGaAloD0MIcsRafIpZaUCUhpRSlGgVTUQBaBZHQKB7RIVdonN1fZQoaAZoCWgPQwj8NVmjHuhuQJSGlFKUaBVNMQFoFkdAoHuwVfu1GHV9lChoBmgJaA9DCLecS3FVQ3BAlIaUUpRoFU21AWgWR0Cge72d3B55dX2UKGgGaAloD0MII9xkVNklcECUhpRSlGgVTQoBaBZHQKCxkurZJ051fZQoaAZoCWgPQwhB8s6hjAZwQJSGlFKUaBVNVAFoFkdAoLHuDJ2dNHV9lChoBmgJaA9DCHdmguEciHBAlIaUUpRoFU0YAWgWR0Cgsnu3MINWdX2UKGgGaAloD0MIB7R0BdtSbECUhpRSlGgVTTIBaBZHQKCy78Sf16F1fZQoaAZoCWgPQwi3Yn/ZPbVsQJSGlFKUaBVNDwFoFkdAoLNsAWBSUHV9lChoBmgJaA9DCNcYdEJo7GBAlIaUUpRoFU3oA2gWR0Cgs5Mj/uLKdX2UKGgGaAloD0MIamrZWh/kcECUhpRSlGgVTWUBaBZHQKCz2KRdQfp1fZQoaAZoCWgPQwjpRe1+VfVwQJSGlFKUaBVNKgFoFkdAoLRubPQfIXV9lChoBmgJaA9DCJ/m5EUmkG5AlIaUUpRoFU0YAWgWR0CgtNU34sVddX2UKGgGaAloD0MIRnu8kI6dbECUhpRSlGgVTRABaBZHQKC1LPPcBU91fZQoaAZoCWgPQwho7Es2HmJqQJSGlFKUaBVNJgFoFkdAoLWc52hZhnV9lChoBmgJaA9DCMhe7/54/GxAlIaUUpRoFU0eAWgWR0CgteFuvUz9dX2UKGgGaAloD0MIrhBWY4kbcECUhpRSlGgVTQIBaBZHQKC17bwBo251fZQoaAZoCWgPQwifPCzUGt5wQJSGlFKUaBVNYgJoFkdAoLYENhE0BXV9lChoBmgJaA9DCEvMs5LWC29AlIaUUpRoFU0hAWgWR0CgtrehPCVKdX2UKGgGaAloD0MIborHRTVfaUCUhpRSlGgVTUEBaBZHQKC4HK9wm3R1fZQoaAZoCWgPQwgYJegv9P5vQJSGlFKUaBVNKAFoFkdAoLgtXFLnLnV9lChoBmgJaA9DCHwOLEdItWpAlIaUUpRoFU0SAWgWR0CguRVTBInSdX2UKGgGaAloD0MIgsR29wD9bkCUhpRSlGgVTSsCaBZHQKC5ucKgIyF1fZQoaAZoCWgPQwjgopOl1q5wQJSGlFKUaBVNSgFoFkdAoLoSIP9UCXV9lChoBmgJaA9DCD4IAfnSTHBAlIaUUpRoFU0HAWgWR0CgulYZl4C7dX2UKGgGaAloD0MIOlyrPey5MECUhpRSlGgVS8doFkdAoLpvechC+nV9lChoBmgJaA9DCNF2TN0VyWxAlIaUUpRoFU1kAWgWR0CguoKIBRyfdX2UKGgGaAloD0MIvt798d5xYUCUhpRSlGgVTegDaBZHQKC7ixUNrj51fZQoaAZoCWgPQwjwT6kSZTZtQJSGlFKUaBVNXgFoFkdAoLvlitq59XV9lChoBmgJaA9DCO8Bui/n6G9AlIaUUpRoFU0fAWgWR0CgvIOuaF23dX2UKGgGaAloD0MIn1kSoCbEb0CUhpRSlGgVTS8BaBZHQKC+YJTER8N1fZQoaAZoCWgPQwixprIo7CtvQJSGlFKUaBVNDQFoFkdAoL9pLVWjoXV9lChoBmgJaA9DCKLw2To4GD9AlIaUUpRoFUvyaBZHQKDA4TqSowV1fZQoaAZoCWgPQwidSDDVzBxEQJSGlFKUaBVL/2gWR0CgwXDdgv12dX2UKGgGaAloD0MIRUlIpO2obUCUhpRSlGgVTV0BaBZHQKDBs1vVEux1fZQoaAZoCWgPQwjXTSmvFUFwQJSGlFKUaBVNMQFoFkdAoMIZ/PPcBXV9lChoBmgJaA9DCFBwsaKGVm9AlIaUUpRoFU0eAWgWR0Cgwlpsfq5cdX2UKGgGaAloD0MIEEBqE+ePcECUhpRSlGgVTQMBaBZHQKDCtRO1v2p1fZQoaAZoCWgPQwgJbTmX4mtsQJSGlFKUaBVNNAFoFkdAoMLMsJ6Y3XV9lChoBmgJaA9DCBajrrV3iWtAlIaUUpRoFU2KAWgWR0Cgw4CyprDZdX2UKGgGaAloD0MIO6xwy0cebkCUhpRSlGgVTSEBaBZHQKDDoObRWtF1fZQoaAZoCWgPQwiCqzyBsKhsQJSGlFKUaBVNEQFoFkdAoMPIKOT7mHV9lChoBmgJaA9DCLdhFASP0W5AlIaUUpRoFU1kAmgWR0CgxKqEeyRkdX2UKGgGaAloD0MI1bK1vsjNbECUhpRSlGgVTRYBaBZHQKDFroYekpJ1fZQoaAZoCWgPQwiPxwxUxi9gQJSGlFKUaBVN6ANoFkdAoMa+BOHnEHV9lChoBmgJaA9DCJaX/E++HnBAlIaUUpRoFU0XAWgWR0CgxyoQnQY2dX2UKGgGaAloD0MIQrKACVyqbkCUhpRSlGgVTRYBaBZHQKDHWI2OyVx1fZQoaAZoCWgPQwjvyi4YXBRsQJSGlFKUaBVNDAFoFkdAoMd8tK7I1nV9lChoBmgJaA9DCN1B7EwhWW9AlIaUUpRoFU1HAWgWR0Cgx6xffGdadX2UKGgGaAloD0MIzLOSVnzPb0CUhpRSlGgVTQ8BaBZHQKDHxIre67N1fZQoaAZoCWgPQwgFptO6jS5wQJSGlFKUaBVNCgFoFkdAoMfyWVu76HV9lChoBmgJaA9DCMzxCkRP921AlIaUUpRoFU0KAWgWR0CgyKOdXko4dX2UKGgGaAloD0MI2ubG9EQjcECUhpRSlGgVTQIBaBZHQKDIxGc4HX51fZQoaAZoCWgPQwh7oYDt4L5sQJSGlFKUaBVNOgFoFkdAoMjuC/XXiHV9lChoBmgJaA9DCIy7QbRW6EBAlIaUUpRoFUvsaBZHQKDJRm9xp+N1fZQoaAZoCWgPQwhoXDgQEtNgQJSGlFKUaBVN6ANoFkdAoMtxZwGW2XV9lChoBmgJaA9DCPQz9brFsWFAlIaUUpRoFU3oA2gWR0Cgy/efRNRFdX2UKGgGaAloD0MIpdsSuWDdcECUhpRSlGgVTQMBaBZHQKDMMTLW7OF1fZQoaAZoCWgPQwjyfAbUm69vQJSGlFKUaBVNJgFoFkdAoMx+e4Cp33V9lChoBmgJaA9DCJPF/Uemwz1AlIaUUpRoFU0HAWgWR0CgzJpDu0CzdX2UKGgGaAloD0MI3zXoS+/Kb0CUhpRSlGgVTRkBaBZHQKDNPhZQpF11fZQoaAZoCWgPQwhtVn2uNhRuQJSGlFKUaBVNEAFoFkdAoM1DYXfqHHV9lChoBmgJaA9DCD83NGVnlHBAlIaUUpRoFU02AWgWR0CgzWNpEhJRdX2UKGgGaAloD0MIdR2qKQkKcUCUhpRSlGgVS/ZoFkdAoM2TWI42j3V9lChoBmgJaA9DCHxETImkk2tAlIaUUpRoFU0dAWgWR0CgzmGPPszEdX2UKGgGaAloD0MIUBpqFBLObkCUhpRSlGgVTW8BaBZHQKDPoq/dqL11fZQoaAZoCWgPQwhBR6taUh1uQJSGlFKUaBVNDwFoFkdAoNCYL/jsEHV9lChoBmgJaA9DCDnv/+OE7m5AlIaUUpRoFU1dAmgWR0Cg0YKoIfKZdX2UKGgGaAloD0MI0ZMyqSHFbECUhpRSlGgVTQICaBZHQKDRltGd7OV1fZQoaAZoCWgPQwgZG7rZn+JtQJSGlFKUaBVNOQFoFkdAoNIIRsdkrnV9lChoBmgJaA9DCF4td2bCAXFAlIaUUpRoFU0gAWgWR0Cg0ifYJ3PidX2UKGgGaAloD0MIZtgo6zdFbUCUhpRSlGgVTUQBaBZHQKDSdRplBhR1fZQoaAZoCWgPQwhcO1ESEr9nQJSGlFKUaBVNRQFoFkdAoNLA3xWkrXV9lChoBmgJaA9DCBLYnINnGGFAlIaUUpRoFU3oA2gWR0Cg0yLBj4HpdX2UKGgGaAloD0MInOCbpo9TcECUhpRSlGgVTTcBaBZHQKDTMS5AhSt1fZQoaAZoCWgPQwg02xX64JxxQJSGlFKUaBVNPQFoFkdAoNNF/x2B8XV9lChoBmgJaA9DCLfRAN4CGW1AlIaUUpRoFU0jAWgWR0Cg09ssQNCrdX2UKGgGaAloD0MI7ginBe89cECUhpRSlGgVS/9oFkdAoNaBlWfbsXV9lChoBmgJaA9DCO3Xne68em9AlIaUUpRoFU1CAWgWR0Cg100euFHsdX2UKGgGaAloD0MIt7bwvFQZXkCUhpRSlGgVTegDaBZHQKDXXK5Cngp1fZQoaAZoCWgPQwhLsaNxqGs8QJSGlFKUaBVNAwFoFkdAoNeSyIHkcXV9lChoBmgJaA9DCA37PbHO5m5AlIaUUpRoFU0VAWgWR0Cg1+Hn2ZiNdX2UKGgGaAloD0MI8DFYcep2cUCUhpRSlGgVTTQBaBZHQKDYIEkB0ZF1fZQoaAZoCWgPQwizQLtDyuZwQJSGlFKUaBVNDAFoFkdAoNifx6OYIHV9lChoBmgJaA9DCCgPC7Umh29AlIaUUpRoFU0+AWgWR0Cg2YDoyKvWdX2UKGgGaAloD0MIcxHfidktb0CUhpRSlGgVTSIBaBZHQKDZz80k4WF1fZQoaAZoCWgPQwhCeLRxBJlxQJSGlFKUaBVNOgFoFkdAoNpUpG4I8nV9lChoBmgJaA9DCJFFmngHDGxAlIaUUpRoFU0oAWgWR0Cg2xPVVghKdWUu"
74
+ },
75
+ "ep_success_buffer": {
76
+ ":type:": "<class 'collections.deque'>",
77
+ ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
78
+ },
79
+ "_n_updates": 310,
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
+ }
ppo-lunarlander-1e6/policy.optimizer.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:5246e0ab1f83601fc06ff9b1a21ed0658655ccda9237b7b0a13afcaa93133e29
3
+ size 87929
ppo-lunarlander-1e6/policy.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:90c37a6e0cab71a65e27958b4302a42ae318352c6094a662504bd3ebd0eb5196
3
+ size 43393
ppo-lunarlander-1e6/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-1e6/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: 1.22.4
7
+ - Gym: 0.21.0
replay.mp4 ADDED
Binary file (197 kB). View file
 
results.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"mean_reward": 241.11964506232124, "std_reward": 17.379868532735987, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-23T17:31:33.492141"}