![]() In all other cases, no preprocessor will be used and the raw observations from the environment Producing -1.0 to 1.0 values (instead of 0.0 to 1.0 values by default).Ītari RAM observations (1D space of shape (128, )) are zero-averaged Grayscale=True for reducing the color channel to 1, or zero_mean=True for Images of shape (210, 160, 3) are downscaled to dim x dim, whereĭim is a model config key (see default Model config below). The following mappings apply for Atari-type observation spaces: However, if the Algorithm’s config key preprocessor_pref is set to “rllib”, Observations: dict_or_tuple_obs = restore_original_dimensions(input_dict, self.obs_space, "tf|torch")įor Atari observation spaces, RLlib defaults to using the DeepMind preprocessors put this into your loss function to access the original Sub-spaces are handled as described above.Īlso, the original dict/tuple observations are still available inside a) the Model via the inputĭict’s “obs” key (the flattened observations are in “obs_flat”), as well as b) the Policy ![]() Tuple and Dict observations are flattened, thereby, Discrete and MultiDiscrete these two vectors are then concatenated to. The first 1 is encoded as and the second 3 is encoded as MultiDiscrete observations are encoded by one-hot encoding each discrete elementĪnd then concatenating the respective one-hot encoded vectors.Į.g. Thereby, the following simple rules apply:ĭiscrete observations are one-hot encoded, e.g. RLlib tries to pick one of its built-in preprocessors based on the environment’s Default Behaviors # Built-in Preprocessors # RLlib internal, which means they can only be modified by changing the algorithm Implementations, as described in the next sections. The components highlighted in green can be replaced with custom user-defined Interpreted by an ActionDistribution to determine the next action. for running mean normalization)īefore being sent to a neural network Model. The observation is preprocessed by a Preprocessor and Filter (e.g. We start with an Environment, which - given an action - produces an observation. ![]() The following diagram provides a conceptual overview of data flow between different components in RLlib. Models, Preprocessors, and Action Distributions # ![]() Gradually replacing the ModelV2 API and some convoluted parts of Policy API with the RLModule API.Ĭlick here for details. From Ray 2.6.0 onwards, RLlib is adopting a new stack for training and model customization, ![]()
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