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Found 6 papers, 2 papers with code
IE-GAN: An Improved Evolutionary Generative Adversarial Network Using a New Fitness Function and a Generic Crossover Operator
The training of generative adversarial networks (GANs) is usually vulnerable to mode collapse and vanishing gradients.
Evolutionary Generative Adversarial Networks with Crossover Based Knowledge Distillation
Generative Adversarial Networks (GAN) is an adversarial model, and it has been demonstrated to be effective for various generative tasks.
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.
