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 +16 -16
- 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:
|
14 |
name: mean_reward
|
15 |
task:
|
16 |
type: reinforcement-learning
|
|
|
10 |
results:
|
11 |
- metrics:
|
12 |
- type: mean_reward
|
13 |
+
value: 142.05 +/- 77.13
|
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 0x7f487cd1fdc0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f487cd1fe50>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f487cd1fee0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f487cd1ff70>", "_build": "<function ActorCriticPolicy._build at 0x7f487cd23040>", "forward": "<function ActorCriticPolicy.forward at 0x7f487cd230d0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f487cd23160>", "_predict": "<function ActorCriticPolicy._predict at 0x7f487cd231f0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f487cd23280>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f487cd23310>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f487cd233a0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f487ce2f040>"}, "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": 1652400452.1997762, "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": 0.01, "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 0x7fa789c20dc0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fa789c20e50>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fa789c20ee0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fa789c20f70>", "_build": "<function ActorCriticPolicy._build at 0x7fa789c23040>", "forward": "<function ActorCriticPolicy.forward at 0x7fa789c230d0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fa789c23160>", "_predict": "<function ActorCriticPolicy._predict at 0x7fa789c231f0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fa789c23280>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fa789c23310>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fa789c233a0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fa78a705c80>"}, "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": 1652401184.524122, "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": 0.98, "ent_coef": 0.01, "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 sha256:5bc3b70e60a6fad67933c80a73eaa45a8224232dda0537d6d1b8bbf976536ac2
|
3 |
+
size 143937
|
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": {},
|
@@ -47,7 +47,7 @@
|
|
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'>",
|
@@ -69,7 +69,7 @@
|
|
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'>",
|
@@ -78,7 +78,7 @@
|
|
78 |
"_n_updates": 310,
|
79 |
"n_steps": 2048,
|
80 |
"gamma": 0.999,
|
81 |
-
"gae_lambda": 0.
|
82 |
"ent_coef": 0.01,
|
83 |
"vf_coef": 0.5,
|
84 |
"max_grad_norm": 0.5,
|
|
|
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 0x7fa789c20dc0>",
|
8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fa789c20e50>",
|
9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fa789c20ee0>",
|
10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fa789c20f70>",
|
11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7fa789c23040>",
|
12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7fa789c230d0>",
|
13 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fa789c23160>",
|
14 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7fa789c231f0>",
|
15 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fa789c23280>",
|
16 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fa789c23310>",
|
17 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7fa789c233a0>",
|
18 |
"__abstractmethods__": "frozenset()",
|
19 |
+
"_abc_impl": "<_abc._abc_data object at 0x7fa78a705c80>"
|
20 |
},
|
21 |
"verbose": 1,
|
22 |
"policy_kwargs": {},
|
|
|
47 |
"_num_timesteps_at_start": 0,
|
48 |
"seed": null,
|
49 |
"action_noise": null,
|
50 |
+
"start_time": 1652401184.524122,
|
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'>",
|
|
|
78 |
"_n_updates": 310,
|
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,
|
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 sha256:446ff4df98e08d9286bcab255d075b1a62fda6037a2b04e25574153da6b11baf
|
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 sha256:288c8a711f0254197fd8cca884bb27fb5ecae3a2dfd38012e1e8966cfff44642
|
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:ecc8d56259624802e6f7b60f55aed02ed3dd5dfa557582add2bcd31ce539aa2e
|
3 |
+
size 224751
|
results.json
CHANGED
@@ -1 +1 @@
|
|
1 |
-
{"mean_reward":
|
|
|
1 |
+
{"mean_reward": 142.05072749278625, "std_reward": 77.12916993562649, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-12T17:29:33.534952"}
|