d3rlpy.metrics.AverageValueEstimationEvaluator¶
- class d3rlpy.metrics.AverageValueEstimationEvaluator(*args, **kwds)[source]¶
Returns average value estimation.
This metrics suggests the scale for estimation of Q functions. If average value estimation is too large, the Q functions overestimate action-values, which possibly makes training failed.
\[\mathbb{E}_{s_t \sim D} [ \max_a Q_\theta (s_t, a)]\]- Parameters
episodes – Optional evaluation episodes. If it’s not given, dataset used in training will be used.
Methods
- __call__(algo, dataset)[source]¶
Computes metrics.
- Parameters
algo (d3rlpy.interface.QLearningAlgoProtocol) – Q-learning algorithm.
dataset (d3rlpy.dataset.replay_buffer.ReplayBuffer) – ReplayBuffer.
- Returns
Computed metrics.
- Return type