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Found 8 papers, 0 papers with code
Sample Efficient Reinforcement Learning by Automatically Learning to Compose Subtasks
In this work, we propose an RL algorithm that can automatically structure the reward function for sample efficiency, given a set of labels that signify subtasks.
On the Opportunities of Green Computing: A Survey
To tackle the challenges of computing resources and environmental impact of AI, Green Computing has become a hot research topic.
Approaching epidemiological dynamics of COVID-19 with physics-informed neural networks
A physics-informed neural network (PINN) embedded with the susceptible-infected-removed (SIR) model is devised to understand the temporal evolution dynamics of infectious diseases.
VCE: Variational Convertor-Encoder for One-Shot Generalization
Variational Convertor-Encoder (VCE) converts an image to various styles; we present this novel architecture for the problem of one-shot generalization and its transfer to new tasks not seen before without additional training.
Proximal Policy Optimization via Enhanced Exploration Efficiency
Based on continuous control tasks with dense reward, this paper analyzes the assumption of the original Gaussian action exploration mechanism in PPO algorithm, and clarifies the influence of exploration ability on performance.
Regularly Updated Deterministic Policy Gradient Algorithm
Deep Deterministic Policy Gradient (DDPG) algorithm is one of the most well-known reinforcement learning methods.
NROWAN-DQN: A Stable Noisy Network with Noise Reduction and Online Weight Adjustment for Exploration
Effective exploration for noisy networks is one of the most important issues in deep reinforcement learning.
Recruitment-imitation Mechanism for Evolutionary Reinforcement Learning
In this paper, we propose Recruitment-imitation Mechanism (RIM) for evolutionary reinforcement learning, a scalable framework that combines advantages of the three methods mentioned above.
