d3rlpy.models.q_functions
d3rlpy provides various Q functions including state-of-the-arts, which are internally used in algorithm objects. You can switch Q functions by passing q_func_factory
argument at algorithm initialization.
from d3rlpy.algos import CQL
cql = CQL(q_func_factory='qr') # use Quantile Regression Q function
Also you can change hyper parameters.
from d3rlpy.models.q_functions import QRQFunctionFactory
q_func = QRQFunctionFactory(n_quantiles=32)
cql = CQL(q_func_factory=q_func)
The default Q function is mean
approximator, which estimates expected scalar action-values. However, in recent advancements of deep reinforcement learning, the new type of action-value approximators has been proposed, which is called distributional Q functions.
Unlike the mean
approximator, the distributional Q functions estimate distribution of action-values. This distributional approaches have shown consistently much stronger performance than the mean
approximator.
Here is a list of available Q functions in the order of performance ascendingly. Currently, as a trade-off between performance and computational complexity, the higher performance requires the more expensive computational costs.
d3rlpy.models.q_functions.MeanQFunctionFactory d3rlpy.models.q_functions.QRQFunctionFactory d3rlpy.models.q_functions.IQNQFunctionFactory d3rlpy.models.q_functions.FQFQFunctionFactory