Reinforcement Learning
Distributional Policy Evaluation: a Maximum Entropy approach to Representation Learning
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.
A Long N-step Surrogate Stage Reward for Deep Reinforcement Learning Junmin Zhong Arizona State University Ruofan Wu Arizona State University Jennie Si Arizona State University
DDPG (TD3) [7], have demonstrated their great potential. Contributions . 1) We introduce a new, simple yet effective surrogate reward They usually proceed as follows.
Constraint-Conditioned Policy Optimization for Versatile Safe Reinforcement Learning
CCPO can generalize to diverse unseen constraint thresholds without retraining the policy.
A Partially Supervised Reinforcement Learning Framework for Visual Active 440 Search: Supplementary Material 441 A Policy Network Architecture and Hyperparameter Details
Table 13: ANT comparisons when trained with small car as target on xView in multi-query setting.
A Partially Supervised Reinforcement Learning Framework for Visual Active Search
Moreover, query results (e.g., detected search and rescue activity in a particular region) are highly informative about the locations of target objects in other regions, for example, due to spatial