d3rlpy.metrics.CompareDiscreteActionMatchEvaluator¶
- class d3rlpy.metrics.CompareDiscreteActionMatchEvaluator(base_algo, episodes=None)[source]¶
Action matches between algorithms.
This metric suggests how different the two algorithms are in discrete action-space. If the algorithm to compare with is near-optimal, the small action difference would be better.
\[\mathbb{E}_{s_t \sim D} [\parallel \{\text{argmax}_a Q_{\theta_1}(s_t, a) = \text{argmax}_a Q_{\theta_2}(s_t, a)\}]\]- Parameters:
base_algo – Target algorithm to comapre with.
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 (QLearningAlgoProtocol) – Q-learning algorithm.
dataset (ReplayBufferBase) – ReplayBuffer.
- Returns:
Computed metrics.
- Return type: