jaxdem.rl.models.mingru#
Implementation of reinforcement learning models based on MinGRU.
Classes
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A recurrent actor-critic with an MLP encoder and a MinGRU torso. |
- class jaxdem.rl.models.mingru.MinGRUActorCritic(*args: Any, **kwargs: Any)[source]#
Bases:
ModelA recurrent actor-critic with an MLP encoder and a MinGRU torso.
This model uses the MinGRU architecture (https://arxiv.org/abs/2410.01201) and relies on a parallel associative scan for sequence-mode training, enabling extremely fast execution.
- Parameters:
observation_space_size (int) – Flattened observation size (
obs_dim).action_space_size (int) – Number of action dimensions (for continuous) or number of discrete actions.
key (nnx.Rngs) – Random number generator(s) for parameter initialization.
hidden_features (int) – Width of the encoder output.
gru_features (int) – MinGRU hidden size. Also the feature size consumed by the policy and value heads.
num_layers (int) – Number of MinGRU layers.
activation (Callable) – Activation function applied inside the encoder.
action_space (distrax.Bijector | ActionSpace | None) – Bijector to constrain the policy probability distribution (continuous only).
remat (bool) – Accepted for compatibility but unused since associative scan is memory efficient.
actor_sigma_head (bool) – If
True, the standard deviation is produced by a learned head on the MinGRU output; otherwise an independent log-std parameter is used. Only used for continuous actions.carry_leading_shape (tuple[int, ...]) – Leading dimensions for the persistent carry tensor
h.discrete (bool) – If True, use a categorical distribution for discrete actions.