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.

import d3rlpy

cql = d3rlpy.algos.CQLConfig(q_func_factory=d3rlpy.models.QRQFunctionFactory())

Also you can change hyper parameters.

q_func = d3rlpy.models.QRQFunctionFactory(n_quantiles=32)

cql = d3rlpy.algos.CQLConfig(q_func_factory=q_func).create()

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.MeanQFunctionFactory`	Standard Q function factory class.
`d3rlpy.models.QRQFunctionFactory`	Quantile Regression Q function factory class.
`d3rlpy.models.IQNQFunctionFactory`	Implicit Quantile Network Q function factory class.