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A Survey on Physics Informed Reinforcement Learning: Review and Open Problems
We present a thorough review of the literature on incorporating physics information, as known as physics priors, in reinforcement learning approaches, commonly referred to as physics-informed reinforcement learning (PIRL).
Physics-Informed Computer Vision: A Review and Perspectives
We present a systematic literature review of formulation and approaches to computer vision tasks guided by physical laws.
Boosting Exploration in Actor-Critic Algorithms by Incentivizing Plausible Novel States
Actor-critic (AC) algorithms are a class of model-free deep reinforcement learning algorithms, which have proven their efficacy in diverse domains, especially in solving continuous control problems.
Improved Soft Actor-Critic: Mixing Prioritized Off-Policy Samples with On-Policy Experience
It is comparatively more stable and sample efficient when tested on a number of continuous control tasks in MuJoCo environments.
Optimal Actor-Critic Policy with Optimized Training Datasets
Actor-critic (AC) algorithms are known for their efficacy and high performance in solving reinforcement learning problems, but they also suffer from low sampling efficiency.
