Initial commit

ppo-QbertNoFrameskip-v4.zip

@@ -1,3 +1,3 @@
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ppo-QbertNoFrameskip-v4/data

@@ -4,9 +4,9 @@
         ":serialized:": "gASVPgAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMFEFjdG9yQ3JpdGljQ25uUG9saWN5lJOULg==",
         "__module__": "stable_baselines3.common.policies",
         "__doc__": "\n    CNN 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 ActorCriticCnnPolicy.__init__ at 0x7fe90c7d2cb0>",
+        "__init__": "<function ActorCriticCnnPolicy.__init__ at 0x7efbe99b0cb0>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc_data object at 0x7fe90c83c5a0>"
+        "_abc_impl": "<_abc_data object at 0x7efbe9a195a0>"
     },
     "verbose": 1,
     "policy_kwargs": {},

results.json

@@ -1 +1 @@
-{"mean_reward": 12830.0, "std_reward": 4355.309977487251, "is_deterministic": false, "n_eval_episodes": 10, "eval_datetime": "2022-06-16T14:22:50.589372"}
+{"mean_reward": 12830.0, "std_reward": 4355.309977487251, "is_deterministic": false, "n_eval_episodes": 10, "eval_datetime": "2022-06-16T14:26:19.046195"}