FIT17 commited on
Commit
5e875b5
1 Parent(s): 7f45fa0

Upload PPO LunarLander-v2 trained agent

Browse files
FIT17.zip ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:d82f53273b8901f0f187f94adca6bc97c40c9c85898ecb0a46942b1f17606106
3
+ size 147150
FIT17/_stable_baselines3_version ADDED
@@ -0,0 +1 @@
 
 
1
+ 1.6.1
FIT17/data ADDED
@@ -0,0 +1,94 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 sde_net_arch: Network architecture for extracting features\n when using gSDE. If None, the latent features from the policy will be used.\n Pass an empty list to use the states as features.\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 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 0x7f6eb66395f0>",
8
+ "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f6eb6639680>",
9
+ "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f6eb6639710>",
10
+ "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f6eb66397a0>",
11
+ "_build": "<function ActorCriticPolicy._build at 0x7f6eb6639830>",
12
+ "forward": "<function ActorCriticPolicy.forward at 0x7f6eb66398c0>",
13
+ "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f6eb6639950>",
14
+ "_predict": "<function ActorCriticPolicy._predict at 0x7f6eb66399e0>",
15
+ "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f6eb6639a70>",
16
+ "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f6eb6639b00>",
17
+ "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f6eb6639b90>",
18
+ "__abstractmethods__": "frozenset()",
19
+ "_abc_impl": "<_abc_data object at 0x7f6eb667fc30>"
20
+ },
21
+ "verbose": 1,
22
+ "policy_kwargs": {},
23
+ "observation_space": {
24
+ ":type:": "<class 'gym.spaces.box.Box'>",
25
+ ":serialized:": "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",
26
+ "dtype": "float32",
27
+ "_shape": [
28
+ 8
29
+ ],
30
+ "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]",
31
+ "high": "[inf inf inf inf inf inf inf inf]",
32
+ "bounded_below": "[False False False False False False False False]",
33
+ "bounded_above": "[False False False False False False False False]",
34
+ "_np_random": null
35
+ },
36
+ "action_space": {
37
+ ":type:": "<class 'gym.spaces.discrete.Discrete'>",
38
+ ":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
39
+ "n": 4,
40
+ "_shape": [],
41
+ "dtype": "int64",
42
+ "_np_random": null
43
+ },
44
+ "n_envs": 16,
45
+ "num_timesteps": 507904,
46
+ "_total_timesteps": 500000,
47
+ "_num_timesteps_at_start": 0,
48
+ "seed": null,
49
+ "action_noise": null,
50
+ "start_time": 1664540424609212755,
51
+ "learning_rate": 0.0003,
52
+ "tensorboard_log": null,
53
+ "lr_schedule": {
54
+ ":type:": "<class 'function'>",
55
+ ":serialized:": "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"
56
+ },
57
+ "_last_obs": {
58
+ ":type:": "<class 'numpy.ndarray'>",
59
+ ":serialized:": "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"
60
+ },
61
+ "_last_episode_starts": {
62
+ ":type:": "<class 'numpy.ndarray'>",
63
+ ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="
64
+ },
65
+ "_last_original_obs": null,
66
+ "_episode_num": 0,
67
+ "use_sde": false,
68
+ "sde_sample_freq": -1,
69
+ "_current_progress_remaining": -0.015808000000000044,
70
+ "ep_info_buffer": {
71
+ ":type:": "<class 'collections.deque'>",
72
+ ":serialized:": "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"
73
+ },
74
+ "ep_success_buffer": {
75
+ ":type:": "<class 'collections.deque'>",
76
+ ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
77
+ },
78
+ "_n_updates": 124,
79
+ "n_steps": 1024,
80
+ "gamma": 0.999,
81
+ "gae_lambda": 0.98,
82
+ "ent_coef": 0.01,
83
+ "vf_coef": 0.5,
84
+ "max_grad_norm": 0.5,
85
+ "batch_size": 64,
86
+ "n_epochs": 4,
87
+ "clip_range": {
88
+ ":type:": "<class 'function'>",
89
+ ":serialized:": "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"
90
+ },
91
+ "clip_range_vf": null,
92
+ "normalize_advantage": true,
93
+ "target_kl": null
94
+ }
FIT17/policy.optimizer.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:836684aa7af2f5e22d8718c9e8cd8b66f18a9f82d7231ee8a725a1bf4721ab81
3
+ size 87865
FIT17/policy.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:88b8db841e32aa2ae396e61375ac66f7ad9b6c35c458a075d701f2811f02f7ed
3
+ size 43201
FIT17/pytorch_variables.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
3
+ size 431
FIT17/system_info.txt ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ OS: Linux-5.10.133+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Fri Aug 26 08:44:51 UTC 2022
2
+ Python: 3.7.14
3
+ Stable-Baselines3: 1.6.1
4
+ PyTorch: 1.12.1+cu113
5
+ GPU Enabled: True
6
+ Numpy: 1.21.6
7
+ Gym: 0.21.0
README.md ADDED
@@ -0,0 +1,36 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
+ - metrics:
12
+ - type: mean_reward
13
+ value: 123.84 +/- 87.24
14
+ name: mean_reward
15
+ task:
16
+ type: reinforcement-learning
17
+ name: reinforcement-learning
18
+ dataset:
19
+ name: LunarLander-v2
20
+ type: LunarLander-v2
21
+ ---
22
+
23
+ # **PPO** Agent playing **LunarLander-v2**
24
+ This is a trained model of a **PPO** agent playing **LunarLander-v2**
25
+ using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
26
+
27
+ ## Usage (with Stable-baselines3)
28
+ TODO: Add your code
29
+
30
+
31
+ ```python
32
+ from stable_baselines3 import ...
33
+ from huggingface_sb3 import load_from_hub
34
+
35
+ ...
36
+ ```
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 sde_net_arch: Network architecture for extracting features\n when using gSDE. If None, the latent features from the policy will be used.\n Pass an empty list to use the states as features.\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 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 0x7f6eb66395f0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f6eb6639680>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f6eb6639710>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f6eb66397a0>", "_build": "<function ActorCriticPolicy._build at 0x7f6eb6639830>", "forward": "<function ActorCriticPolicy.forward at 0x7f6eb66398c0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f6eb6639950>", "_predict": "<function ActorCriticPolicy._predict at 0x7f6eb66399e0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f6eb6639a70>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f6eb6639b00>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f6eb6639b90>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f6eb667fc30>"}, "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:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": 4, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "num_timesteps": 507904, "_total_timesteps": 500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1664540424609212755, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAAJqIDb18JCo/QMRWvd+Lbr5NPuG9brdPvgAAAAAAAAAAmvdZva5Stz9tDdK+QrCIvdenH72OED2+AAAAAAAAAAD2fxY/rB3yPCUhVrr+Mn+33uCrPPV+gzkAAIA/AACAP0ZxMD422TW8PGeCuWvcIji7GZ69/vmrOAAAgD8AAIA/kz0VPptROD8YDmc82haAvo/jMjz+7lO9AAAAAAAAAABmV6a9sYedPe78aj71Why+Be+Lve4S0T0AAAAAAAAAADNO0LykYBS5uw/KO76zuDhiMpa6CpF/ugAAgD8AAIA/DVf5PYWvnDq63XK7piKvuSU6CDwpcZK6AACAPwAAgD/K7tY+lcPYPnBvHTvcizu+/DCSvUMBdjwAAAAAAAAAAE0tsT6Xi0i9Qv5hunD3DTmcS2i+mFqIOQAAgD8AAIA/iEsJP7EXJz74IP29biOlvFlq/D0Q4Wi9AACAPwAAgD9NZC69rlGbugTDDzyM+I43JPB8Osj/eTYAAIA/AACAP81iG71cLzq63kCGt4la6jQiCvK61h2XNgAAgD8AAIA/TeLVPR89rrnYAKQ6G1hLNkherTtRisG5AACAPwAAgD8zXwo8JlWwP8L3jT5Hj/K+FrBCvNYss70AAAAAAAAAAE1yBb29p5g/qy6pvURuj77BezW9UifivQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////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:": "gAWVfBAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIhLcHISAzSUCUhpRSlIwBbJRN6AOMAXSUR0CBh9CtRvWIdX2UKGgGaAloD0MI+1sC8E+1X0CUhpRSlGgVTegDaBZHQIGR4ztTkyV1fZQoaAZoCWgPQwjmIynpYUpaQJSGlFKUaBVN6ANoFkdAgZSNQbdadXV9lChoBmgJaA9DCCs0EMtmyFpAlIaUUpRoFU3oA2gWR0CBn6eS0Sh8dX2UKGgGaAloD0MIGjGzz2MMP0CUhpRSlGgVTegDaBZHQIGkiAOJ+Dx1fZQoaAZoCWgPQwgQy2YOSd08QJSGlFKUaBVLxGgWR0CBrzO0svqUdX2UKGgGaAloD0MI0a3X9KAKW0CUhpRSlGgVTegDaBZHQIG8RxkupS91fZQoaAZoCWgPQwhws3ixsCNjQJSGlFKUaBVN6ANoFkdAgcf4u01IiHV9lChoBmgJaA9DCOFdLuI7ol9AlIaUUpRoFU3oA2gWR0CB7hb9qDbrdX2UKGgGaAloD0MIWHA/4IEyYUCUhpRSlGgVTegDaBZHQIHxzJZGKAJ1fZQoaAZoCWgPQwgXghyUsKliQJSGlFKUaBVN6ANoFkdAgfMD0L+glHV9lChoBmgJaA9DCOT1YFJ8b2JAlIaUUpRoFU3oA2gWR0CB/SVSGahIdX2UKGgGaAloD0MIXAGFevrASMCUhpRSlGgVS9toFkdAgf/F/YraunV9lChoBmgJaA9DCCrJOhxds1VAlIaUUpRoFU3oA2gWR0CCBsJa7mMgdX2UKGgGaAloD0MIH0jeOZTFVUCUhpRSlGgVTegDaBZHQIIIsKeCkGl1fZQoaAZoCWgPQwj7IMuCifs9QJSGlFKUaBVLsmgWR0CCCfMxoIv8dX2UKGgGaAloD0MIonprYKtoXkCUhpRSlGgVTegDaBZHQIIXJ+BpYcN1fZQoaAZoCWgPQwjt2AjE6/tSQJSGlFKUaBVN6ANoFkdAghnPkq+ajXV9lChoBmgJaA9DCDbqIRrdZUNAlIaUUpRoFU3oA2gWR0CCGg/8EV32dX2UKGgGaAloD0MIlGsKZHY3UMCUhpRSlGgVS51oFkdAgiq4gaFVUHV9lChoBmgJaA9DCE7VPbK5w15AlIaUUpRoFU3oA2gWR0CCM+Yl6Z6VdX2UKGgGaAloD0MI8db5t8uSVUCUhpRSlGgVTegDaBZHQII+fdyksSV1fZQoaAZoCWgPQwh8mShCaqNgQJSGlFKUaBVN6ANoFkdAgkxwwj+rEXV9lChoBmgJaA9DCDkNUYU/TWJAlIaUUpRoFU3oA2gWR0CCUbozN2TxdX2UKGgGaAloD0MINpNvtrkvXUCUhpRSlGgVTegDaBZHQIJc921UlzF1fZQoaAZoCWgPQwjnUfF/R/1WQJSGlFKUaBVN6ANoFkdAgnW6+vhZQ3V9lChoBmgJaA9DCH/C2a1l6mJAlIaUUpRoFU3oA2gWR0CCdwr/bTMJdX2UKGgGaAloD0MIjuVd9YCxX0CUhpRSlGgVTegDaBZHQIKf3TZxrBV1fZQoaAZoCWgPQwgoucMmMuFVwJSGlFKUaBVNaAFoFkdAgqp7Qswta3V9lChoBmgJaA9DCFab/1cdrlxAlIaUUpRoFU3oA2gWR0CCq8GtZFG5dX2UKGgGaAloD0MIm+RH/IoXXkCUhpRSlGgVTegDaBZHQIKuO/i5uqF1fZQoaAZoCWgPQwieQxmq4rJgQJSGlFKUaBVN6ANoFkdAgrSwJHAh0XV9lChoBmgJaA9DCG5uTE9YpltAlIaUUpRoFU3oA2gWR0CCtooLG7z1dX2UKGgGaAloD0MIrYcvE0X5XECUhpRSlGgVTegDaBZHQIK3u6Zpi7V1fZQoaAZoCWgPQwiFtMagE9JVQJSGlFKUaBVN6ANoFkdAgsXibtqpLnV9lChoBmgJaA9DCLIPsiyYylRAlIaUUpRoFU3oA2gWR0CCxiC7sfJWdX2UKGgGaAloD0MIDOiFOxeGEMCUhpRSlGgVS7ZoFkdAgsuYkeIVM3V9lChoBmgJaA9DCO+OjNXm+UnAlIaUUpRoFU0FAWgWR0CC0/RfnfVJdX2UKGgGaAloD0MIu9Vz0vuTYECUhpRSlGgVTegDaBZHQILV9xCIDYB1fZQoaAZoCWgPQwgGLSRg9CNhwJSGlFKUaBVNBgJoFkdAgtnVfu1F6XV9lChoBmgJaA9DCMtMaf0t+FdAlIaUUpRoFU3oA2gWR0CC3fLh73PBdX2UKGgGaAloD0MIQRGLGHbETkCUhpRSlGgVTegDaBZHQILmBGWldkd1fZQoaAZoCWgPQwhaY9AJoQddQJSGlFKUaBVN6ANoFkdAgvI7XpW3jXV9lChoBmgJaA9DCBtJgnAFtAPAlIaUUpRoFU0aAWgWR0CC90nrpqyodX2UKGgGaAloD0MIhCugUE8rRMCUhpRSlGgVTTsBaBZHQIL98CDEm6Z1fZQoaAZoCWgPQwhJ88e0NnhYQJSGlFKUaBVN6ANoFkdAgwIKv/zasnV9lChoBmgJaA9DCD9z1qccR1NAlIaUUpRoFU3oA2gWR0CDLdI91U2ldX2UKGgGaAloD0MInu3RG+7WXkCUhpRSlGgVTegDaBZHQIMx+ws5GSZ1fZQoaAZoCWgPQwh6NNWTeYBiQJSGlFKUaBVN6ANoFkdAg2RHfl6qsHV9lChoBmgJaA9DCBr9aDjlz2BAlIaUUpRoFU3oA2gWR0CDZagxJul5dX2UKGgGaAloD0MIFsJqLGEFPkCUhpRSlGgVTegDaBZHQINoWGoJiRZ1fZQoaAZoCWgPQwju0LAYdT5fQJSGlFKUaBVN6ANoFkdAg29GrKeTV3V9lChoBmgJaA9DCJ268lkeEmXAlIaUUpRoFU3AA2gWR0CDf2KlYU35dX2UKGgGaAloD0MIp88OuK4jWkCUhpRSlGgVTegDaBZHQIOErxG2Cul1fZQoaAZoCWgPQwhZNQhzu9lZQJSGlFKUaBVN6ANoFkdAg4vcWKuSwHV9lChoBmgJaA9DCGGKcmn8jVxAlIaUUpRoFU3oA2gWR0CDnGq94/u9dX2UKGgGaAloD0MIryKjA5JYWkCUhpRSlGgVTegDaBZHQIOg2eBg/kh1fZQoaAZoCWgPQwiUT49tGSZjQJSGlFKUaBVN6ANoFkdAg6rTjFQ2uXV9lChoBmgJaA9DCBlz1xLyLTrAlIaUUpRoFU2YA2gWR0CDsrfKISDidX2UKGgGaAloD0MI63JKQEySJUCUhpRSlGgVTegDaBZHQIO4aBTXJ5p1fZQoaAZoCWgPQwge3J21211cQJSGlFKUaBVN6ANoFkdAg8RF9Sde6nV9lChoBmgJaA9DCAvrxrsjyFlAlIaUUpRoFU3oA2gWR0CDyBvUjLSvdX2UKGgGaAloD0MI+fTYlgGMVMCUhpRSlGgVTVUBaBZHQIPY6jYZl4F1fZQoaAZoCWgPQwiKyoY1lYleQJSGlFKUaBVN6ANoFkdAg9/Q4S6DoXV9lChoBmgJaA9DCHMSSl8IzFJAlIaUUpRoFU3oA2gWR0CD41mUW2w3dX2UKGgGaAloD0MInz4Cf/hlX0CUhpRSlGgVTegDaBZHQIQS82NvOyF1fZQoaAZoCWgPQwgCY30Dk7lcQJSGlFKUaBVN6ANoFkdAhBRC4jKPn3V9lChoBmgJaA9DCEW6n1MQs2BAlIaUUpRoFU3oA2gWR0CEFrf1pTMrdX2UKGgGaAloD0MIVwT/W8k/VkCUhpRSlGgVTeQDaBZHQIQcZD5TIeZ1fZQoaAZoCWgPQwjmP6TfvlxaQJSGlFKUaBVN6ANoFkdAhCtEBCD28XV9lChoBmgJaA9DCGozTkNUUWZAlIaUUpRoFU3oA2gWR0CEMCj5bhWHdX2UKGgGaAloD0MIkj6toj90YkCUhpRSlGgVTegDaBZHQIQ2mvKU3XJ1fZQoaAZoCWgPQwjSyOcVT/ZQQJSGlFKUaBVN6ANoFkdAhETyiVSn+HV9lChoBmgJaA9DCPOqzmqBt1ZAlIaUUpRoFU3oA2gWR0CESMt9QXQ/dX2UKGgGaAloD0MI5/7qcd/gWkCUhpRSlGgVTegDaBZHQIRY/IXCTEB1fZQoaAZoCWgPQwgH8BZIUABbQJSGlFKUaBVN6ANoFkdAhF6MgMc6vXV9lChoBmgJaA9DCFZhM8AFuRLAlIaUUpRoFU3oA2gWR0CEbVXRPXTWdX2UKGgGaAloD0MIqP+s+fFnUUCUhpRSlGgVTegDaBZHQIRxPwb2lEZ1fZQoaAZoCWgPQwgsY0M3+zFkQJSGlFKUaBVN6ANoFkdAhIJYDDCP63V9lChoBmgJaA9DCCi4WFGDvlRAlIaUUpRoFU3oA2gWR0CEiWxHoX9BdX2UKGgGaAloD0MIU14robuTXkCUhpRSlGgVTegDaBZHQISM72g39751fZQoaAZoCWgPQwj3sBcK2Mo1wJSGlFKUaBVNJwFoFkdAhJVzzd1uBXV9lChoBmgJaA9DCAbzV8hcSVxAlIaUUpRoFU3oA2gWR0CEvINSZSeidX2UKGgGaAloD0MIflaZKS1UYkCUhpRSlGgVTegDaBZHQIS9w+6iCat1fZQoaAZoCWgPQwgEATJ07KhOQJSGlFKUaBVN6ANoFkdAhMAC4axX4nV9lChoBmgJaA9DCLtkHCPZ7FZAlIaUUpRoFU3oA2gWR0CExa8yN4qxdX2UKGgGaAloD0MIgBDJkGPrKMCUhpRSlGgVTegDaBZHQITUyHfuTid1fZQoaAZoCWgPQwjww0FClGNLQJSGlFKUaBVN6ANoFkdAhNn1hCtzS3V9lChoBmgJaA9DCM76lGMymWBAlIaUUpRoFU3oA2gWR0CE4UNtqHoHdX2UKGgGaAloD0MIYDyDhn4PYkCUhpRSlGgVTegDaBZHQITx0lE7W/d1fZQoaAZoCWgPQwjMBwQ6k7JeQJSGlFKUaBVN6ANoFkdAhPY4bCJoCnV9lChoBmgJaA9DCCsXKv9aklZAlIaUUpRoFU3oA2gWR0CFCHLt/nW8dX2UKGgGaAloD0MIQRGLGHauXkCUhpRSlGgVTegDaBZHQIUOYEMb3oN1fZQoaAZoCWgPQwixhovc0zpjQJSGlFKUaBVN6ANoFkdAhR/Bz3h4uHV9lChoBmgJaA9DCOnWa3rQp2BAlIaUUpRoFU3oA2gWR0CFMuxDb8FZdX2UKGgGaAloD0MI2bERiNekXkCUhpRSlGgVTegDaBZHQIU6xIQOFxp1fZQoaAZoCWgPQwiWsgxxrGRfQJSGlFKUaBVN6ANoFkdAhT6ao/A0sXV9lChoBmgJaA9DCGlznNuEYFVAlIaUUpRoFU3oA2gWR0CFR4hC+lCUdX2UKGgGaAloD0MIBi/6ClIYZUCUhpRSlGgVTegDaBZHQIVJTCk43m51fZQoaAZoCWgPQwgDCB9KtHBWQJSGlFKUaBVN6ANoFkdAhUqGdy1eB3VlLg=="}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 124, "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-5.10.133+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Fri Aug 26 08:44:51 UTC 2022", "Python": "3.7.14", "Stable-Baselines3": "1.6.1", "PyTorch": "1.12.1+cu113", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}
replay.mp4 ADDED
Binary file (245 kB). View file
 
results.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"mean_reward": 123.83883288266108, "std_reward": 87.23583866701928, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-09-30T12:30:12.495633"}