However, this setting might be unrealistic as different classes of actions often co-occur inpractice.

For all the experiments, we generate augmentations in the same way as in MoCo v2 [1] for pretraining. The learning rate is set tolr = 0.1 and is gradually annealed following a cosine decay schedule [3]. For linear classification, all models are trained for 100 epochs with alearning rate oflr = 10.0. For each image, we randomly generate 32 augmented images and feed these images into the pre-trained network toextract features. The feature vectors are`2 normalized before computing similarities and variances.

In Distributional Reinforcement Learning (D-RL) [Bellemare et al., 2023], an agent aims to estimate Sutton and Barto, 2018], where the objective is to predict the expected return only. In Section 3, we answer this methodological question, showing that it is possible to reformulate Policy Evaluation in a distributional setting so that its performance index is explicitly intertwined with the representation of the (state or action) spaces.

Wedesigned to: 1) answer anchors 5.2); 2) executed variables graphs ofmillions 5.1 Setup Dataset.

Inthemuch more complex football game, URI's policy beat the built-in AIs with a 37% winning rate while GPT-based agents can only achieve a 6% winning rate.