Upload ppo-LunarLander-v2 model with longer training session
Browse files- README.md +1 -1
- config.json +1 -1
- ppo-LunarLander-v2.zip +2 -2
- ppo-LunarLander-v2/data +18 -18
- ppo-LunarLander-v2/policy.optimizer.pth +1 -1
- ppo-LunarLander-v2/policy.pth +1 -1
- replay.mp4 +2 -2
- results.json +1 -1
README.md
CHANGED
@@ -10,7 +10,7 @@ model-index:
|
|
10 |
results:
|
11 |
- metrics:
|
12 |
- type: mean_reward
|
13 |
-
value: 268.
|
14 |
name: mean_reward
|
15 |
task:
|
16 |
type: reinforcement-learning
|
|
|
10 |
results:
|
11 |
- metrics:
|
12 |
- type: mean_reward
|
13 |
+
value: 268.12 +/- 21.13
|
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 0x7fc1109e0dc0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fc1109e0e50>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fc1109e0ee0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fc1109e0f70>", "_build": "<function ActorCriticPolicy._build at 0x7fc1109e3040>", "forward": "<function ActorCriticPolicy.forward at 0x7fc1109e30d0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fc1109e3160>", "_predict": "<function ActorCriticPolicy._predict at 0x7fc1109e31f0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fc1109e3280>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fc1109e3310>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fc1109e33a0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fc110ae14c0>"}, "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": 8, "num_timesteps": 507904, "_total_timesteps": 500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1652405796.8662019, "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:": "gAWVewAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYIAAAAAAAAAAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksIhZSMAUOUdJRSlC4="}, "_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": 0.999, "gae_lambda": 0.95, "ent_coef": 0.05, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 32, "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-5.15.0-27-generic-x86_64-with-glibc2.35 #28-Ubuntu SMP Thu Apr 14 04:55:28 UTC 2022", "Python": "3.9.12", "Stable-Baselines3": "1.5.0", "PyTorch": "1.11.0", "GPU Enabled": "True", "Numpy": "1.22.3", "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 0x7f743461edc0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f743461ee50>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f743461eee0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f743461ef70>", "_build": "<function ActorCriticPolicy._build at 0x7f7434624040>", "forward": "<function ActorCriticPolicy.forward at 0x7f74346240d0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f7434624160>", "_predict": "<function ActorCriticPolicy._predict at 0x7f74346241f0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f7434624280>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f7434624310>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f74346243a0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f7434623340>"}, "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": 8, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1652406546.667636, "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:": "gAWVewAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYIAAAAAAAAAAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksIhZSMAUOUdJRSlC4="}, "_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": 620, "n_steps": 2048, "gamma": 0.999, "gae_lambda": 0.95, "ent_coef": 0.05, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 32, "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-5.15.0-27-generic-x86_64-with-glibc2.35 #28-Ubuntu SMP Thu Apr 14 04:55:28 UTC 2022", "Python": "3.9.12", "Stable-Baselines3": "1.5.0", "PyTorch": "1.11.0", "GPU Enabled": "True", "Numpy": "1.22.3", "Gym": "0.21.0"}}
|
ppo-LunarLander-v2.zip
CHANGED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
-
size
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:46a452c4b673f8ed995c5e92dc8b9d2cfb53b4a5f10fe75f611b2b8d524a6977
|
3 |
+
size 143939
|
ppo-LunarLander-v2/data
CHANGED
@@ -4,19 +4,19 @@
|
|
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
|
8 |
-
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at
|
9 |
-
"reset_noise": "<function ActorCriticPolicy.reset_noise at
|
10 |
-
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at
|
11 |
-
"_build": "<function ActorCriticPolicy._build at
|
12 |
-
"forward": "<function ActorCriticPolicy.forward at
|
13 |
-
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at
|
14 |
-
"_predict": "<function ActorCriticPolicy._predict at
|
15 |
-
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at
|
16 |
-
"get_distribution": "<function ActorCriticPolicy.get_distribution at
|
17 |
-
"predict_values": "<function ActorCriticPolicy.predict_values at
|
18 |
"__abstractmethods__": "frozenset()",
|
19 |
-
"_abc_impl": "<_abc._abc_data object at
|
20 |
},
|
21 |
"verbose": 1,
|
22 |
"policy_kwargs": {},
|
@@ -42,12 +42,12 @@
|
|
42 |
"_np_random": null
|
43 |
},
|
44 |
"n_envs": 8,
|
45 |
-
"num_timesteps":
|
46 |
-
"_total_timesteps":
|
47 |
"_num_timesteps_at_start": 0,
|
48 |
"seed": null,
|
49 |
"action_noise": null,
|
50 |
-
"start_time":
|
51 |
"learning_rate": 0.0003,
|
52 |
"tensorboard_log": null,
|
53 |
"lr_schedule": {
|
@@ -56,7 +56,7 @@
|
|
56 |
},
|
57 |
"_last_obs": {
|
58 |
":type:": "<class 'numpy.ndarray'>",
|
59 |
-
":serialized:": "
|
60 |
},
|
61 |
"_last_episode_starts": {
|
62 |
":type:": "<class 'numpy.ndarray'>",
|
@@ -69,13 +69,13 @@
|
|
69 |
"_current_progress_remaining": -0.015808000000000044,
|
70 |
"ep_info_buffer": {
|
71 |
":type:": "<class 'collections.deque'>",
|
72 |
-
":serialized:": "
|
73 |
},
|
74 |
"ep_success_buffer": {
|
75 |
":type:": "<class 'collections.deque'>",
|
76 |
":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
|
77 |
},
|
78 |
-
"_n_updates":
|
79 |
"n_steps": 2048,
|
80 |
"gamma": 0.999,
|
81 |
"gae_lambda": 0.95,
|
|
|
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 0x7f743461edc0>",
|
8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f743461ee50>",
|
9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f743461eee0>",
|
10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f743461ef70>",
|
11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7f7434624040>",
|
12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7f74346240d0>",
|
13 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f7434624160>",
|
14 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7f74346241f0>",
|
15 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f7434624280>",
|
16 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f7434624310>",
|
17 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7f74346243a0>",
|
18 |
"__abstractmethods__": "frozenset()",
|
19 |
+
"_abc_impl": "<_abc._abc_data object at 0x7f7434623340>"
|
20 |
},
|
21 |
"verbose": 1,
|
22 |
"policy_kwargs": {},
|
|
|
42 |
"_np_random": null
|
43 |
},
|
44 |
"n_envs": 8,
|
45 |
+
"num_timesteps": 1015808,
|
46 |
+
"_total_timesteps": 1000000,
|
47 |
"_num_timesteps_at_start": 0,
|
48 |
"seed": null,
|
49 |
"action_noise": null,
|
50 |
+
"start_time": 1652406546.667636,
|
51 |
"learning_rate": 0.0003,
|
52 |
"tensorboard_log": null,
|
53 |
"lr_schedule": {
|
|
|
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'>",
|
|
|
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": 620,
|
79 |
"n_steps": 2048,
|
80 |
"gamma": 0.999,
|
81 |
"gae_lambda": 0.95,
|
ppo-LunarLander-v2/policy.optimizer.pth
CHANGED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
size 84893
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:79481778d15f3ba0200242cb9735d1a8d6600848f9e7fbcb7587fdc898c8a5dd
|
3 |
size 84893
|
ppo-LunarLander-v2/policy.pth
CHANGED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
size 43201
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:511937f4ec08ec184872c345a181e877fdd981bb404594793a3125b28ea09048
|
3 |
size 43201
|
replay.mp4
CHANGED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
-
size
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:9a443de2479072d875704460d05b33aca827b9fd9f76576eea133864a1e159f5
|
3 |
+
size 206163
|
results.json
CHANGED
@@ -1 +1 @@
|
|
1 |
-
{"mean_reward": 268.
|
|
|
1 |
+
{"mean_reward": 268.12190353576943, "std_reward": 21.128915618973398, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-12T19:10:20.852091"}
|