culteejen commited on
Commit
7d822e9
1 Parent(s): 549020b

Upload model to Hugging Face

Browse files
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  "__doc__": "\n Policy class with Q-Value Net and target net for DQN\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 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 ",
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72
  },
73
  "ep_success_buffer": {
74
  ":type:": "<class 'collections.deque'>",
75
  ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
76
  },
77
- "_n_updates": 15625,
78
  "buffer_size": 100000,
79
  "batch_size": 32,
80
  "learning_starts": 50000,
@@ -87,12 +87,12 @@
87
  ":serialized:": "gAWVNQAAAAAAAACMIHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5idWZmZXJzlIwMUmVwbGF5QnVmZmVylJOULg==",
88
  "__module__": "stable_baselines3.common.buffers",
89
  "__doc__": "\n Replay buffer used in off-policy algorithms like SAC/TD3.\n\n :param buffer_size: Max number of element in the buffer\n :param observation_space: Observation space\n :param action_space: Action space\n :param device: PyTorch device\n :param n_envs: Number of parallel environments\n :param optimize_memory_usage: Enable a memory efficient variant\n of the replay buffer which reduces by almost a factor two the memory used,\n at a cost of more complexity.\n See https://github.com/DLR-RM/stable-baselines3/issues/37#issuecomment-637501195\n and https://github.com/DLR-RM/stable-baselines3/pull/28#issuecomment-637559274\n Cannot be used in combination with handle_timeout_termination.\n :param handle_timeout_termination: Handle timeout termination (due to timelimit)\n separately and treat the task as infinite horizon task.\n https://github.com/DLR-RM/stable-baselines3/issues/284\n ",
90
- "__init__": "<function ReplayBuffer.__init__ at 0x7f73edf730a0>",
91
- "add": "<function ReplayBuffer.add at 0x7f73edf73130>",
92
- "sample": "<function ReplayBuffer.sample at 0x7f73edf731c0>",
93
- "_get_samples": "<function ReplayBuffer._get_samples at 0x7f73edf73250>",
94
  "__abstractmethods__": "frozenset()",
95
- "_abc_impl": "<_abc._abc_data object at 0x7f73ee361640>"
96
  },
97
  "replay_buffer_kwargs": {},
98
  "train_freq": {
@@ -105,7 +105,7 @@
105
  "exploration_final_eps": 0.05,
106
  "exploration_fraction": 0.1,
107
  "target_update_interval": 625,
108
- "_n_calls": 75000,
109
  "max_grad_norm": 10,
110
  "exploration_rate": 0.05,
111
  "exploration_schedule": {
 
4
  ":serialized:": "gAWVMAAAAAAAAACMHnN0YWJsZV9iYXNlbGluZXMzLmRxbi5wb2xpY2llc5SMCURRTlBvbGljeZSTlC4=",
5
  "__module__": "stable_baselines3.dqn.policies",
6
  "__doc__": "\n Policy class with Q-Value Net and target net for DQN\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 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 DQNPolicy.__init__ at 0x7fe229c73490>",
8
+ "_build": "<function DQNPolicy._build at 0x7fe229c73520>",
9
+ "make_q_net": "<function DQNPolicy.make_q_net at 0x7fe229c735b0>",
10
+ "forward": "<function DQNPolicy.forward at 0x7fe229c73640>",
11
+ "_predict": "<function DQNPolicy._predict at 0x7fe229c736d0>",
12
+ "_get_constructor_parameters": "<function DQNPolicy._get_constructor_parameters at 0x7fe229c73760>",
13
+ "set_training_mode": "<function DQNPolicy.set_training_mode at 0x7fe229c737f0>",
14
  "__abstractmethods__": "frozenset()",
15
+ "_abc_impl": "<_abc._abc_data object at 0x7fe229c7f480>"
16
  },
17
  "verbose": true,
18
  "policy_kwargs": {},
 
31
  },
32
  "action_space": {
33
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