Generalized attention-weighted reinforcement learning

Highly valued subgoal generation for efficient goal-conditioned reinforcement learning

Goal-conditioned reinforcement learning is widely used in robot control, manipulating the robot to accomplish specific tasks by maximizing accumulated rewards. However, the useful reward signal is only received when the desired goal is reached, leading to the issue of sparse rewards and affecting the efficiency of policy learning. In this paper, we propose a method to generate highly valued subgoals for efficient goal-conditioned policy learning, enabling the development of smart home robots or automatic pilots in our daily life. The highly valued subgoals are conditioned on the context of the specific tasks and characterized by suitable complexity for efficient goal-conditioned action value learning. The context variable captures the latent representation of the particular tasks, allowing for efficient subgoal generation. Additionally, the goal-conditioned action values regularized by the self-adaptive ranges generate subgoals with suitable complexity. Compared to Hindsight Experience Replay that uniformly samples subgoals from visited trajectories, our method generates the subgoals based on the context of tasks with suitable difficulty for efficient policy training. Experimental results show that our method achieves stable performance in robotic environments compared to baseline methods.

A Survey on Explainable Artificial Intelligence (XAI) Techniques for Visualizing Deep Learning Models in Medical Imaging

The combination of medical imaging and deep learning has significantly improved diagnostic and prognostic capabilities in the healthcare domain. Nevertheless, the inherent complexity of deep learning models poses challenges in understanding their decision-making processes. Interpretability and visualization techniques have emerged as crucial tools to unravel the black-box nature of these models, providing insights into their inner workings and enhancing trust in their predictions. This survey paper comprehensively examines various interpretation and visualization techniques applied to deep learning models in medical imaging. The paper reviews methodologies, discusses their applications, and evaluates their effectiveness in enhancing the interpretability, reliability, and clinical relevance of deep learning models in medical image analysis.

Salience Interest Option: Temporal abstraction with salience interest functions

Reinforcement Learning (RL) is a significant machine learning subfield that emphasizes learning actions based on environment to obtain optimal behavior policy. RL agents can make decisions at variable time scales in the form of temporal abstractions, also known as options. The issue of discovering options has seen a considerable research effort. Most notably, the Interest Option Critic (IOC) algorithm first extends the initial set to the interest function, providing a method for learning options specialized to certain state space regions. This approach offers a specific attention mechanism for action selection. Unfortunately, this method still suffers from the classic issues of poor data efficiency and lack of flexibility in RL when learning options end-to-end through backpropagation. This paper proposes a new approach called Salience Interest Option Critic (SIOC), which chooses subsets of existing initiation sets for RL. Specifically, these subsets are not learned by backpropagation, which is slow and tends to overfit, but through particle filters. This approach enables the rapid and flexible identification of critical subsets using only reward feedback. We conducted experiments in discrete and continuous domains, and our proposed method demonstrate higher efficiency and flexibility than other methods. The generated options are more valuable within a single task and exhibited greater interpretability and reusability in multi-task learning scenarios.