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remove minibatch_size attribute to SAC trainer #655

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remove minibatch_size attribute to SAC trainer #655

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3 changes: 0 additions & 3 deletions reagent/training/dqn_trainer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -43,7 +43,6 @@ def __init__(
rl: RLParameters = field(default_factory=RLParameters), # noqa: B008
double_q_learning: bool = True,
bcq: Optional[BCQConfig] = None,
minibatch_size: int = 1024,
minibatches_per_step: int = 1,
optimizer: Optimizer__Union = field( # noqa: B008
default_factory=Optimizer__Union.default
Expand All @@ -62,7 +61,6 @@ def __init__(
rl: RLParameters
double_q_learning: boolean flag to use double-q learning
bcq: a config file for batch-constrained q-learning, defaults to normal
minibatch_size: samples per minibatch
minibatches_per_step: minibatch updates per step
optimizer: q-network optimizer
evaluation: evaluation params, primarily whether to use CPE in eval or not
Expand All @@ -75,7 +73,6 @@ def __init__(
)
assert self._actions is not None, "Discrete-action DQN needs action names"
self.double_q_learning = double_q_learning
self.minibatch_size = minibatch_size
self.minibatches_per_step = minibatches_per_step or 1

self.q_network = q_network
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1 change: 0 additions & 1 deletion reagent/training/sac_trainer.py
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Expand Up @@ -74,7 +74,6 @@ def __init__(
alpha_optimizer: Optional[Optimizer__Union] = field( # noqa: B008
default_factory=Optimizer__Union.default
),
minibatch_size: int = 1024,
entropy_temperature: float = 0.01,
logged_action_uniform_prior: bool = True,
target_entropy: float = -1.0,
Expand Down