serin32 commited on
Commit
c204788
1 Parent(s): 8a5da68

Upload PPO LunarLander-v2 trained agent

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: 266.38 +/- 22.56
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 0x7f77c0d5b670>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f77c0d5b700>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f77c0d5b790>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f77c0d5b820>", "_build": "<function ActorCriticPolicy._build at 0x7f77c0d5b8b0>", "forward": "<function ActorCriticPolicy.forward at 0x7f77c0d5b940>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f77c0d5b9d0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f77c0d5ba60>", "_predict": "<function ActorCriticPolicy._predict at 0x7f77c0d5baf0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f77c0d5bb80>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f77c0d5bc10>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f77c0d5bca0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f77c0d538a0>"}, "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": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1673557035790322288, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/M6kqMFUyYYWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAABpokj3fu6A8jsyNvalOMb50a6y88kjCPAAAAAAAAAAAGoiSvQPALz+mC5k9gFzSvrIhr7xiGPM9AAAAAAAAAAAm1ZK9JGwnPq1mVj7z126+rY+nvPbFhD0AAAAAAAAAAMDBmb1SGNS5axrhuhffhLaDPio7NnYDOgAAAAAAAIA/mpVIPg2K4j7puSS+r7trvumJnDyO3uG8AAAAAAAAAAADTV2+uCKiPoS9gj6blmm+rZGEPKYmCzwAAAAAAAAAAObjuT1ca1u6+HY3MwbuWq9ZOYq6LJK/swAAgD8AAIA/KoaDPr86az+CxVw+wqvOvoBZez4gEbw7AAAAAAAAAAAAKJm89mweuq4oy7a+EgCy1nB7Oug38DUAAIA/AACAP7N2Ab4GC/g+LCg7u0Ohrr6/F4m9ZpN4PQAAAAAAAAAAzYzWuwN9J7yje9m8Fm8PPYUBjD3C3Oe9AACAPwAAgD8zYzE7jypkunm/C7XEPgevR/aEu30kYzQAAIA/AACAP02MtT3IJIM/yyOGPq6M4r4a8es9OhbYPQAAAAAAAAAAZsscvSNecz3jVow+yn0xvshJtz0MFpA9AAAAAAAAAADAvV8+4MiqPmBOXL6112O+Aej2PJo98rwAAAAAAAAAAJplm7vbq0Q/5mDsPDFr1L4xcra8qr0RPQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////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": 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:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/yZmZmZmZmoWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.10.147+-x86_64-with-glibc2.27 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.16", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.0+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 sha256:15ad9301ddd48d26c5548cf1f4d59ecd67368229b79cb1e25393d3b888edc4ac
3
+ size 147408
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 0x7f77c0d5b670>",
8
+ "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f77c0d5b700>",
9
+ "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f77c0d5b790>",
10
+ "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f77c0d5b820>",
11
+ "_build": "<function ActorCriticPolicy._build at 0x7f77c0d5b8b0>",
12
+ "forward": "<function ActorCriticPolicy.forward at 0x7f77c0d5b940>",
13
+ "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f77c0d5b9d0>",
14
+ "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f77c0d5ba60>",
15
+ "_predict": "<function ActorCriticPolicy._predict at 0x7f77c0d5baf0>",
16
+ "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f77c0d5bb80>",
17
+ "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f77c0d5bc10>",
18
+ "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f77c0d5bca0>",
19
+ "__abstractmethods__": "frozenset()",
20
+ "_abc_impl": "<_abc_data object at 0x7f77c0d538a0>"
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": 1015808,
47
+ "_total_timesteps": 1000000,
48
+ "_num_timesteps_at_start": 0,
49
+ "seed": null,
50
+ "action_noise": null,
51
+ "start_time": 1673557035790322288,
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:": "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"
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 sha256:1d3bc4a98d306865f3a1754e900a966f642ca13e20f8cde5b8aad2509628f5b9
3
+ size 87929
ppo-LunarLander-v2/policy.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:8c47f7989cdc6606544241b59978eef84345d5fce4073205d854f09aad79ed21
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-glibc2.27 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
2
+ - Python: 3.8.16
3
+ - Stable-Baselines3: 1.7.0
4
+ - PyTorch: 1.13.0+cu116
5
+ - GPU Enabled: True
6
+ - Numpy: 1.21.6
7
+ - Gym: 0.21.0
replay.mp4 ADDED
Binary file (256 kB). View file
 
results.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"mean_reward": 266.3845766883832, "std_reward": 22.55794906167105, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-12T21:41:44.333396"}