Initial commit

dqn-SpaceInvadersNoFrameskip-v4.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:7c5eb0808ca9e6f380468ccb277bf571dfe11d8bd4a1484314723b4fda7b99ce
+oid sha256:19fef6af425ff5d31ee228e8429a0138940cf9d94255b629f3013fb3123bb0a4
 size 27219665

dqn-SpaceInvadersNoFrameskip-v4/data

@@ -4,9 +4,9 @@
         ":serialized:": "gAWVMAAAAAAAAACMHnN0YWJsZV9iYXNlbGluZXMzLmRxbi5wb2xpY2llc5SMCUNublBvbGljeZSTlC4=",
         "__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 0x123fede10>",
+        "__init__": "<function CnnPolicy.__init__ at 0x123bc1e10>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x123ff6200>"
+        "_abc_impl": "<_abc._abc_data object at 0x123bc9d80>"
     },
     "verbose": 1,
     "policy_kwargs": {},
@@ -84,13 +84,13 @@
         "__module__": "stable_baselines3.common.buffers",
         "__annotations__": "{'observations': <class 'numpy.ndarray'>, 'next_observations': <class 'numpy.ndarray'>, 'actions': <class 'numpy.ndarray'>, 'rewards': <class 'numpy.ndarray'>, 'dones': <class 'numpy.ndarray'>, 'timeouts': <class 'numpy.ndarray'>}",
         "__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 0x123ceb640>",
-        "add": "<function ReplayBuffer.add at 0x123ceb6d0>",
-        "sample": "<function ReplayBuffer.sample at 0x123ceb760>",
-        "_get_samples": "<function ReplayBuffer._get_samples at 0x123ceb7f0>",
-        "_maybe_cast_dtype": "<staticmethod(<function ReplayBuffer._maybe_cast_dtype at 0x123ceb880>)>",
+        "__init__": "<function ReplayBuffer.__init__ at 0x1238bf640>",
+        "add": "<function ReplayBuffer.add at 0x1238bf6d0>",
+        "sample": "<function ReplayBuffer.sample at 0x1238bf760>",
+        "_get_samples": "<function ReplayBuffer._get_samples at 0x1238bf7f0>",
+        "_maybe_cast_dtype": "<staticmethod(<function ReplayBuffer._maybe_cast_dtype at 0x1238bf880>)>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x123c8d700>"
+        "_abc_impl": "<_abc._abc_data object at 0x123865500>"
     },
     "replay_buffer_kwargs": {},
     "train_freq": {

env_kwargs.yml

@@ -1,2 +1 @@
 render_mode: rgb_array
-render_fps: 4

results.json

@@ -1 +1 @@
-{"mean_reward": 427.5, "std_reward": 232.0156244738703, "is_deterministic": false, "n_eval_episodes": 10, "eval_datetime": "2024-03-21T21:53:00.369535"}
+{"mean_reward": 427.5, "std_reward": 232.0156244738703, "is_deterministic": false, "n_eval_episodes": 10, "eval_datetime": "2024-03-21T22:01:43.333889"}