LilOpa commited on
Commit
8a48fcf
1 Parent(s): 3704bd5

PPO LunarLander-v2 trained agent

Browse files
LunarLander_PPO_agent_v2.zip ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:4403ca3363db612b7d3e9d16fbd96abd5f13a8a33c580e0dace35f8540e7cdf8
3
+ size 146422
LunarLander_PPO_agent_v2/_stable_baselines3_version ADDED
@@ -0,0 +1 @@
 
 
1
+ 1.6.0
LunarLander_PPO_agent_v2/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 0x7f1796bcb3b0>",
8
+ "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f1796bcb440>",
9
+ "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f1796bcb4d0>",
10
+ "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f1796bcb560>",
11
+ "_build": "<function ActorCriticPolicy._build at 0x7f1796bcb5f0>",
12
+ "forward": "<function ActorCriticPolicy.forward at 0x7f1796bcb680>",
13
+ "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f1796bcb710>",
14
+ "_predict": "<function ActorCriticPolicy._predict at 0x7f1796bcb7a0>",
15
+ "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f1796bcb830>",
16
+ "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f1796bcb8c0>",
17
+ "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f1796bcb950>",
18
+ "__abstractmethods__": "frozenset()",
19
+ "_abc_impl": "<_abc_data object at 0x7f1796c10c90>"
20
+ },
21
+ "verbose": 1,
22
+ "policy_kwargs": {},
23
+ "observation_space": {
24
+ ":type:": "<class 'gym.spaces.box.Box'>",
25
+ ":serialized:": "gAWVnwEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLCIWUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWIAAAAAAAAAAAAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/5RoCksIhZSMAUOUdJRSlIwEaGlnaJRoEiiWIAAAAAAAAAAAAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf5RoCksIhZRoFXSUUpSMDWJvdW5kZWRfYmVsb3eUaBIolggAAAAAAAAAAAAAAAAAAACUaAeMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLCIWUaBV0lFKUjA1ib3VuZGVkX2Fib3ZllGgSKJYIAAAAAAAAAAAAAAAAAAAAlGghSwiFlGgVdJRSlIwKX25wX3JhbmRvbZROdWIu",
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": 1,
45
+ "num_timesteps": 600064,
46
+ "_total_timesteps": 600000,
47
+ "_num_timesteps_at_start": 0,
48
+ "seed": null,
49
+ "action_noise": null,
50
+ "start_time": 1660064384.2465706,
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:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAM2Yp7yFi4Q6brdvPSt2ir6Q1KG8BsqbPAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="
60
+ },
61
+ "_last_episode_starts": {
62
+ ":type:": "<class 'numpy.ndarray'>",
63
+ ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="
64
+ },
65
+ "_last_original_obs": null,
66
+ "_episode_num": 0,
67
+ "use_sde": false,
68
+ "sde_sample_freq": -1,
69
+ "_current_progress_remaining": -0.00010666666666669933,
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": 6256,
79
+ "n_steps": 2048,
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": 8,
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
+ }
LunarLander_PPO_agent_v2/policy.optimizer.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:5569c946fe9fb491c9f8866dce7d52c4454c6a2c838f637c53e97c2abc1b2adb
3
+ size 87865
LunarLander_PPO_agent_v2/policy.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:313b122159756a1da8119e2e73a8aa8ee6693425836d897d475ed716280134c1
3
+ size 43201
LunarLander_PPO_agent_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
LunarLander_PPO_agent_v2/system_info.txt ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ OS: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022
2
+ Python: 3.7.13
3
+ Stable-Baselines3: 1.6.0
4
+ PyTorch: 1.12.0+cu113
5
+ GPU Enabled: True
6
+ Numpy: 1.21.6
7
+ Gym: 0.21.0
README.md CHANGED
@@ -10,7 +10,7 @@ model-index:
10
  results:
11
  - metrics:
12
  - type: mean_reward
13
- value: 257.51 +/- 67.61
14
  name: mean_reward
15
  task:
16
  type: reinforcement-learning
 
10
  results:
11
  - metrics:
12
  - type: mean_reward
13
+ value: 278.68 +/- 24.01
14
  name: mean_reward
15
  task:
16
  type: reinforcement-learning
config.json CHANGED
@@ -1 +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 0x7f1796bcb3b0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f1796bcb440>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f1796bcb4d0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f1796bcb560>", "_build": "<function ActorCriticPolicy._build at 0x7f1796bcb5f0>", "forward": "<function ActorCriticPolicy.forward at 0x7f1796bcb680>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f1796bcb710>", "_predict": "<function ActorCriticPolicy._predict at 0x7f1796bcb7a0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f1796bcb830>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f1796bcb8c0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f1796bcb950>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f1796c10c90>"}, "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": 1, "num_timesteps": 1001472, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1660061752.4365375, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAA2Tyb1xJmg/svOvvUTD4b72Nvu9ivqPPAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.0014719999999999178, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVWRAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMI14UfnM8ccUCUhpRSlIwBbJRNAwGMAXSUR0Cg+yreIl+mdX2UKGgGaAloD0MIoib6fFRucECUhpRSlGgVS/1oFkdAoP5TgCOmznV9lChoBmgJaA9DCMRBQpSvWnBAlIaUUpRoFU0FAWgWR0Cg/xPXbuc+dX2UKGgGaAloD0MIngd3Z22eckCUhpRSlGgVS/doFkdAoP+832mHg3V9lChoBmgJaA9DCDOoNjjRdnJAlIaUUpRoFU0JAWgWR0ChAHolt0mudX2UKGgGaAloD0MI5h4SvjdKckCUhpRSlGgVTU4BaBZHQKEBdPzFuNx1fZQoaAZoCWgPQwjsNNJSOaJxQJSGlFKUaBVL8GgWR0ChAh7aqS5idX2UKGgGaAloD0MIOPQWD6+ccECUhpRSlGgVTQIBaBZHQKEC17D2rXF1fZQoaAZoCWgPQwi0OGOYk4dyQJSGlFKUaBVL/GgWR0ChA4rf1pTNdX2UKGgGaAloD0MIdxTnqCMHc0CUhpRSlGgVS/FoFkdAoQaLBKtga3V9lChoBmgJaA9DCGVQbXCiw3FAlIaUUpRoFU0iAWgWR0ChB1i4rjHXdX2UKGgGaAloD0MInnqkwe1XbkCUhpRSlGgVTRoBaBZHQKEIKWUKRdR1fZQoaAZoCWgPQwgNb9bgvc9yQJSGlFKUaBVNDQFoFkdAoQjnN5dGAnV9lChoBmgJaA9DCEMDsWxmvnJAlIaUUpRoFU1VAWgWR0ChCe/rSmZWdX2UKGgGaAloD0MIUWnEzL7kcUCUhpRSlGgVS/doFkdAoQqi53C9AXV9lChoBmgJaA9DCK2FWWgnFnFAlIaUUpRoFU0UAWgWR0ChC3uQQtjDdX2UKGgGaAloD0MIZvZ5jLKucECUhpRSlGgVTQcBaBZHQKEOonaWX1J1fZQoaAZoCWgPQwg2IEJc+dVxQJSGlFKUaBVL9GgWR0ChD1VXNke7dX2UKGgGaAloD0MIl+MViB5EckCUhpRSlGgVS+toFkdAoQ/6HTI/7nV9lChoBmgJaA9DCGJITiauV3BAlIaUUpRoFUvraBZHQKEQoE6kqMF1fZQoaAZoCWgPQwgdsKvJk0ZzQJSGlFKUaBVNJwFoFkdAoRFxRCQcP3V9lChoBmgJaA9DCKOVe4FZ13JAlIaUUpRoFU0fAWgWR0ChEj9Whh6TdX2UKGgGaAloD0MIvady2hN/cUCUhpRSlGgVTQ0BaBZHQKES/jMFEAp1fZQoaAZoCWgPQwhselBQiilxQJSGlFKUaBVNJQFoFkdAoRPaubI91XV9lChoBmgJaA9DCM5uLZOh/nBAlIaUUpRoFU0rAWgWR0ChFwk8ifQKdX2UKGgGaAloD0MICrlSz8KGckCUhpRSlGgVTQEBaBZHQKEXxD50r9V1fZQoaAZoCWgPQwiCVfXyuxJwQJSGlFKUaBVL/GgWR0ChGIOBtk4FdX2UKGgGaAloD0MIL6LtmDq8bkCUhpRSlGgVTTcBaBZHQKEZa0uUUwl1fZQoaAZoCWgPQwhnZJC7yBlwQJSGlFKUaBVNDAFoFkdAoRorM1TBInV9lChoBmgJaA9DCHVyhuLOq3FAlIaUUpRoFU0vAWgWR0ChGwLF4s3AdX2UKGgGaAloD0MIOBJosKlUb0CUhpRSlGgVTRMBaBZHQKEbybFS88N1fZQoaAZoCWgPQwhxAWiULidOQJSGlFKUaBVL3GgWR0ChHsHhjvuxdX2UKGgGaAloD0MI4GWGjTJgcECUhpRSlGgVTT8BaBZHQKEfwemvW6N1fZQoaAZoCWgPQwhDWI0lbIRyQJSGlFKUaBVNFwFoFkdAoSCS6MBIWnV9lChoBmgJaA9DCElIpG18OG9AlIaUUpRoFU0zAWgWR0ChIXmFBY3edX2UKGgGaAloD0MIXXAGf7+wcUCUhpRSlGgVS/JoFkdAoSIn7rLQonV9lChoBmgJaA9DCGzu6H85dXJAlIaUUpRoFU0IAWgWR0ChIuZuqFRHdX2UKGgGaAloD0MID5vIzMUeckCUhpRSlGgVTVABaBZHQKEj1hzeXRh1fZQoaAZoCWgPQwiRKR+CqkRwQJSGlFKUaBVNEAFoFkdAoSb18kUsWnV9lChoBmgJaA9DCHriOVvAE3JAlIaUUpRoFU1NAWgWR0ChJ/PaURnOdX2UKGgGaAloD0MIY9UgzC0jcUCUhpRSlGgVS/poFkdAoSitOdoWYXV9lChoBmgJaA9DCITWw5dJaXBAlIaUUpRoFUvvaBZHQKEpVwHZ9NN1fZQoaAZoCWgPQwg+y/PgrhFxQJSGlFKUaBVNBgFoFkdAoSoaCcwxnHV9lChoBmgJaA9DCHqOyHepoXFAlIaUUpRoFU1EAWgWR0ChKxJyIYWMdX2UKGgGaAloD0MI4GjHDX9Ac0CUhpRSlGgVTQUBaBZHQKEr27eVLSN1fZQoaAZoCWgPQwhuGXCWkmpxQJSGlFKUaBVNGgFoFkdAoSykj9n9N3V9lChoBmgJaA9DCOpCrP4IvnFAlIaUUpRoFUvvaBZHQKEvr6vaDf51fZQoaAZoCWgPQwhHrptSXqZwQJSGlFKUaBVL7WgWR0ChMGFV1fVqdX2UKGgGaAloD0MIMxe4PFZfcECUhpRSlGgVS/VoFkdAoTEWnuRcNnV9lChoBmgJaA9DCMWRByILkXBAlIaUUpRoFU00AWgWR0ChMfs1baAXdX2UKGgGaAloD0MIhXgkXl7DckCUhpRSlGgVTTABaBZHQKEy1N0vGqB1fZQoaAZoCWgPQwj+fjFb8nxxQJSGlFKUaBVL+GgWR0ChM4UDMeOodX2UKGgGaAloD0MIteBFX8FQcUCUhpRSlGgVS/BoFkdAoTQ0+xGDtnV9lChoBmgJaA9DCIwS9Be6M3NAlIaUUpRoFU0hAWgWR0ChN1bNbC79dX2UKGgGaAloD0MI6USCqWZscUCUhpRSlGgVTREBaBZHQKE4F4bCJoF1fZQoaAZoCWgPQwikHMwmwN1xQJSGlFKUaBVL92gWR0ChOM/CZWq+dX2UKGgGaAloD0MIzas6qwVkb0CUhpRSlGgVS/FoFkdAoTl3b48EFHV9lChoBmgJaA9DCBk6dlAJkG5AlIaUUpRoFU0JAWgWR0ChOjj+R5kcdX2UKGgGaAloD0MIb4Jvmj5zckCUhpRSlGgVTRwBaBZHQKE7DEnb7CV1fZQoaAZoCWgPQwi0AG2rWRRyQJSGlFKUaBVL9GgWR0ChO7hA4XGfdX2UKGgGaAloD0MIjbeVXhuVbUCUhpRSlGgVS/hoFkdAoTxqMJhOQHV9lChoBmgJaA9DCKMiTifZRW9AlIaUUpRoFUv+aBZHQKE/fSFXaJ11fZQoaAZoCWgPQwhQbtv3aBpxQJSGlFKUaBVL6GgWR0ChQCOO0b97dX2UKGgGaAloD0MIZOsZwjG6bkCUhpRSlGgVTRkBaBZHQKFA+KaXrt51fZQoaAZoCWgPQwj8qIb9XjpxQJSGlFKUaBVNCwFoFkdAoUG+M+/xlXV9lChoBmgJaA9DCBzuI7dmk3BAlIaUUpRoFU0JAWgWR0ChQo3lS0jUdX2UKGgGaAloD0MIZHjsZ7E8b0CUhpRSlGgVTQsBaBZHQKFDWdPtUn51fZQoaAZoCWgPQwgHfentz6xyQJSGlFKUaBVNAwFoFkdAoUQZZOi35XV9lChoBmgJaA9DCPymsFKB+3JAlIaUUpRoFU0CAWgWR0ChRNIaLn9vdX2UKGgGaAloD0MIvhHdsy7GcECUhpRSlGgVS/9oFkdAoUftrsSkCXV9lChoBmgJaA9DCIOG/gkuvnFAlIaUUpRoFUvtaBZHQKFInXo1UER1fZQoaAZoCWgPQwgOFeP8DfRyQJSGlFKUaBVL9WgWR0ChSVH1OCXhdX2UKGgGaAloD0MIZOdtbPZicECUhpRSlGgVTTUBaBZHQKFKPMr3Cbd1fZQoaAZoCWgPQwg4EmiwKa1yQJSGlFKUaBVNHAFoFkdAoUsGa4MF2XV9lChoBmgJaA9DCEUsYtgh8HBAlIaUUpRoFUvuaBZHQKFLsxptaZB1fZQoaAZoCWgPQwhqoPmcu3RuQJSGlFKUaBVL8GgWR0ChTF92gWaddX2UKGgGaAloD0MIJnMs76oDckCUhpRSlGgVTTQBaBZHQKFNMzYVZcN1fZQoaAZoCWgPQwiTjJyFPc5vQJSGlFKUaBVL+WgWR0ChUFNwR5C4dX2UKGgGaAloD0MITb9EvPUucECUhpRSlGgVTQYBaBZHQKFRE4//vOR1fZQoaAZoCWgPQwjbozfcR/txQJSGlFKUaBVNUQFoFkdAoVILR2KVIXV9lChoBmgJaA9DCACL/PrhTHJAlIaUUpRoFUv3aBZHQKFSv/lQuVZ1fZQoaAZoCWgPQwhrYoGvKMlxQJSGlFKUaBVNIAFoFkdAoVOoNEw353V9lChoBmgJaA9DCI+mejL/9HJAlIaUUpRoFUvwaBZHQKFUU8NhE0B1fZQoaAZoCWgPQwgrL/mffPNyQJSGlFKUaBVL+mgWR0ChVQOcDr7gdX2UKGgGaAloD0MIMBFvnX8qbUCUhpRSlGgVS+xoFkdAoVWsSAYpD3V9lChoBmgJaA9DCKZkOQmlAG1AlIaUUpRoFU0vAWgWR0ChWP3dCVrzdX2UKGgGaAloD0MICyk/qXYRb0CUhpRSlGgVTRcBaBZHQKFZxQ40dil1fZQoaAZoCWgPQwhcHmtGxl5wQJSGlFKUaBVNEgFoFkdAoVqTLB9Cu3V9lChoBmgJaA9DCPJgi90+jXFAlIaUUpRoFU0jAWgWR0ChW2xgy/KydX2UKGgGaAloD0MIcFtbeN4vb0CUhpRSlGgVS/ZoFkdAoVwfTRYzSHV9lChoBmgJaA9DCDQw8rKmd29AlIaUUpRoFU0MAWgWR0ChXOvrfLs9dX2UKGgGaAloD0MI8Z2Y9WJ5cUCUhpRSlGgVTQUBaBZHQKFdqz2OAAh1fZQoaAZoCWgPQwggm+RHvHByQJSGlFKUaBVL+GgWR0ChYLSYoiLVdX2UKGgGaAloD0MIN3Fyv4PJcECUhpRSlGgVTS4BaBZHQKFhicn3L3d1fZQoaAZoCWgPQwioUx7dSMZxQJSGlFKUaBVNHgFoFkdAoWJUPSUkfXV9lChoBmgJaA9DCIKOVrVkWXJAlIaUUpRoFUv6aBZHQKFjCgK4QSV1fZQoaAZoCWgPQwgurBvvDuVxQJSGlFKUaBVNOwFoFkdAoWP6aZx7zHV9lChoBmgJaA9DCMTsZdvpGHFAlIaUUpRoFUvwaBZHQKFksIu5BkZ1fZQoaAZoCWgPQwg83XniebFxQJSGlFKUaBVNJwFoFkdAoWWYCQtBfXV9lChoBmgJaA9DCKPLm8M1DnFAlIaUUpRoFUvtaBZHQKFmRlFMIu51ZS4="}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 3912, "n_steps": 2048, "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": 8, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022", "Python": "3.7.13", "Stable-Baselines3": "1.6.0", "PyTorch": "1.12.0+cu113", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}
 
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 0x7f1796bcb3b0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f1796bcb440>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f1796bcb4d0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f1796bcb560>", "_build": "<function ActorCriticPolicy._build at 0x7f1796bcb5f0>", "forward": "<function ActorCriticPolicy.forward at 0x7f1796bcb680>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f1796bcb710>", "_predict": "<function ActorCriticPolicy._predict at 0x7f1796bcb7a0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f1796bcb830>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f1796bcb8c0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f1796bcb950>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f1796c10c90>"}, "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": 1, "num_timesteps": 600064, "_total_timesteps": 600000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1660064384.2465706, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAM2Yp7yFi4Q6brdvPSt2ir6Q1KG8BsqbPAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.00010666666666669933, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 6256, "n_steps": 2048, "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": 8, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022", "Python": "3.7.13", "Stable-Baselines3": "1.6.0", "PyTorch": "1.12.0+cu113", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}
replay.mp4 CHANGED
Binary files a/replay.mp4 and b/replay.mp4 differ
 
results.json CHANGED
@@ -1 +1 @@
1
- {"mean_reward": 257.5124094818724, "std_reward": 67.6141914071652, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-08-09T16:55:25.858843"}
 
1
+ {"mean_reward": 278.6820474146456, "std_reward": 24.00831764213685, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-08-09T17:25:07.832294"}