Many applications of reinforcement learning can be formalized as goal-conditioned environments, where, in each episode, there is a "goal" that affects the rewards obtained during that episode but does not affect the dynamics. Various techniques have been proposed to improve performance in goal-conditioned environments, such as automatic curriculum generation and goal relabeling. In this work, we explore a connection between off-policy reinforcement learning in goal-conditioned settings and knowledge distillation. In particular: the current Q-value function and the target Q-value estimate are both functions of the goal, and we would like to train the Q-value function to match its target for all goals. We therefore apply Gradient-Based Attention Transfer (Zagoruyko and Komodakis 2017), a knowledge distillation technique, to the Q-function update. We empirically show that this can improve the performance of goal-conditioned off-policy reinforcement learning when the space of goals is high-dimensional. We also show that this technique can be adapted to allow for efficient learning in the case of multiple simultaneous sparse goals, where the agent can attain a reward by achieving any one of a large set of objectives, all specified at test time. Finally, to provide theoretical support, we give examples of classes of environments where (under some assumptions) standard off-policy algorithms such as DDPG require at least O(d^2) replay buffer transitions to learn an optimal policy, while our proposed technique requires only O(d) transitions, where d is the dimensionality of the goal and state space. Code is available at https://github.com/alevine0/ReenGAGE.

In a new study, Florida State University researchers explore the challenges of recruiting and retaining older adults to participate in research. The study also marks the first step of a broad, interdisciplinary FSU effort to increasingly use artificial intelligence in research. In the study, published in The Gerontologist, Associate Professor of Sociology Dawn Carr identified core "motivation clusters" among older adults for research participation. Along with her 12 FSU-based co-authors, Carr suggests that identifying those clusters--"fun seekers" and "research helpers," for example--can guide recruitment and retention strategies. "There is a lack of representation of older adults in research that leads to findings that are skewed," Carr said.