Got config for dqn version 2.1

agents/dqn_v2-1/config.json

@@ -0,0 +1,105 @@
+{
+  "policy_class": {
+    ":type:": "<class 'abc.ABCMeta'>",
+    "__module__": "stable_baselines3.dqn.policies",
+    "__doc__": "\n    Policy class for DQN when using images as input.\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 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 CnnPolicy.__init__ at 0x78b9f7af9cf0>",
+    "__abstractmethods__": "frozenset()",
+    "_abc_impl": "<_abc._abc_data object at 0x78b9f7af3bc0>"
+  },
+  "verbose": 1,
+  "policy_kwargs": {},
+  "num_timesteps": 1500000,
+  "_total_timesteps": 1500000,
+  "_num_timesteps_at_start": 0,
+  "seed": null,
+  "action_noise": null,
+  "start_time": 1715277648289674887,
+  "learning_rate": 0.0002,
+  "tensorboard_log": "./",
+  "_last_obs": {
+    ":type:": "<class 'numpy.ndarray'>"
+  },
+  "_last_episode_starts": {
+    ":type:": "<class 'numpy.ndarray'>"
+  },
+  "_last_original_obs": {
+    ":type:": "<class 'numpy.ndarray'>"
+  },
+  "_episode_num": 2112,
+  "use_sde": false,
+  "sde_sample_freq": -1,
+  "_current_progress_remaining": 0.0,
+  "_stats_window_size": 100,
+  "ep_info_buffer": {
+    ":type:": "<class 'collections.deque'>"
+  },
+  "ep_success_buffer": {
+    ":type:": "<class 'collections.deque'>"
+  },
+  "_n_updates": 362500,
+  "observation_space": {
+    ":type:": "<class 'gymnasium.spaces.box.Box'>",
+    "dtype": "uint8",
+    "bounded_below": "[[[ True  True  True ...  True  True  True]\n  [ True  True  True ...  True  True  True]\n  [ True  True  True ...  True  True  True]\n  ...\n  [ True  True  True ...  True  True  True]\n  [ True  True  True ...  True  True  True]\n  [ True  True  True ...  True  True  True]]\n\n [[ True  True  True ...  True  True  True]\n  [ True  True  True ...  True  True  True]\n  [ True  True  True ...  True  True  True]\n  ...\n  [ True  True  True ...  True  True  True]\n  [ True  True  True ...  True  True  True]\n  [ True  True  True ...  True  True  True]]\n\n [[ True  True  True ...  True  True  True]\n  [ True  True  True ...  True  True  True]\n  [ True  True  True ...  True  True  True]\n  ...\n  [ True  True  True ...  True  True  True]\n  [ True  True  True ...  True  True  True]\n  [ True  True  True ...  True  True  True]]]",
+    "bounded_above": "[[[ True  True  True ...  True  True  True]\n  [ True  True  True ...  True  True  True]\n  [ True  True  True ...  True  True  True]\n  ...\n  [ True  True  True ...  True  True  True]\n  [ True  True  True ...  True  True  True]\n  [ True  True  True ...  True  True  True]]\n\n [[ True  True  True ...  True  True  True]\n  [ True  True  True ...  True  True  True]\n  [ True  True  True ...  True  True  True]\n  ...\n  [ True  True  True ...  True  True  True]\n  [ True  True  True ...  True  True  True]\n  [ True  True  True ...  True  True  True]]\n\n [[ True  True  True ...  True  True  True]\n  [ True  True  True ...  True  True  True]\n  [ True  True  True ...  True  True  True]\n  ...\n  [ True  True  True ...  True  True  True]\n  [ True  True  True ...  True  True  True]\n  [ True  True  True ...  True  True  True]]]",
+    "_shape": [
+      3,
+      250,
+      160
+    ],
+    "low": "[[[0 0 0 ... 0 0 0]\n  [0 0 0 ... 0 0 0]\n  [0 0 0 ... 0 0 0]\n  ...\n  [0 0 0 ... 0 0 0]\n  [0 0 0 ... 0 0 0]\n  [0 0 0 ... 0 0 0]]\n\n [[0 0 0 ... 0 0 0]\n  [0 0 0 ... 0 0 0]\n  [0 0 0 ... 0 0 0]\n  ...\n  [0 0 0 ... 0 0 0]\n  [0 0 0 ... 0 0 0]\n  [0 0 0 ... 0 0 0]]\n\n [[0 0 0 ... 0 0 0]\n  [0 0 0 ... 0 0 0]\n  [0 0 0 ... 0 0 0]\n  ...\n  [0 0 0 ... 0 0 0]\n  [0 0 0 ... 0 0 0]\n  [0 0 0 ... 0 0 0]]]",
+    "high": "[[[255 255 255 ... 255 255 255]\n  [255 255 255 ... 255 255 255]\n  [255 255 255 ... 255 255 255]\n  ...\n  [255 255 255 ... 255 255 255]\n  [255 255 255 ... 255 255 255]\n  [255 255 255 ... 255 255 255]]\n\n [[255 255 255 ... 255 255 255]\n  [255 255 255 ... 255 255 255]\n  [255 255 255 ... 255 255 255]\n  ...\n  [255 255 255 ... 255 255 255]\n  [255 255 255 ... 255 255 255]\n  [255 255 255 ... 255 255 255]]\n\n [[255 255 255 ... 255 255 255]\n  [255 255 255 ... 255 255 255]\n  [255 255 255 ... 255 255 255]\n  ...\n  [255 255 255 ... 255 255 255]\n  [255 255 255 ... 255 255 255]\n  [255 255 255 ... 255 255 255]]]",
+    "low_repr": "0",
+    "high_repr": "255",
+    "_np_random": "Generator(PCG64)"
+  },
+  "action_space": {
+    ":type:": "<class 'gymnasium.spaces.discrete.Discrete'>",
+    "n": "5",
+    "start": "0",
+    "_shape": [],
+    "dtype": "int64",
+    "_np_random": "Generator(PCG64)"
+  },
+  "n_envs": 1,
+  "buffer_size": 60000,
+  "batch_size": 64,
+  "learning_starts": 50000,
+  "tau": 1.0,
+  "gamma": 0.999,
+  "gradient_steps": 1,
+  "optimize_memory_usage": false,
+  "replay_buffer_class": {
+    ":type:": "<class 'abc.ABCMeta'>",
+    "__module__": "stable_baselines3.common.buffers",
+    "__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    ",
+    "__init__": "<function ReplayBuffer.__init__ at 0x78b9f7ad5cf0>",
+    "add": "<function ReplayBuffer.add at 0x78b9f7ad5d80>",
+    "sample": "<function ReplayBuffer.sample at 0x78b9f7ad5e10>",
+    "_get_samples": "<function ReplayBuffer._get_samples at 0x78b9f7ad5ea0>",
+    "_maybe_cast_dtype": "<staticmethod(<function ReplayBuffer._maybe_cast_dtype at 0x78b9f7ad5f30>)>",
+    "__abstractmethods__": "frozenset()",
+    "_abc_impl": "<_abc._abc_data object at 0x78b9f7ad9800>"
+  },
+  "replay_buffer_kwargs": {},
+  "train_freq": {
+    ":type:": "<class 'stable_baselines3.common.type_aliases.TrainFreq'>"
+  },
+  "use_sde_at_warmup": false,
+  "exploration_initial_eps": 1.0,
+  "exploration_final_eps": 0.1,
+  "exploration_fraction": 0.3,
+  "target_update_interval": 1000,
+  "_n_calls": 1500000,
+  "max_grad_norm": 10,
+  "exploration_rate": 0.1,
+  "lr_schedule": {
+    ":type:": "<class 'function'>"
+  },
+  "batch_norm_stats": [],
+  "batch_norm_stats_target": [],
+  "exploration_schedule": {
+    ":type:": "<class 'function'>"
+  }
+}