Synaptic Q-learning
A reinforcement learning algorithm that updates action-value functions based on the Bellman equations at the synaptic level.
Category: Reinforcement Learning
GRPO
An optimization technique used in reinforcement learning to improve policy performance based on feedback from the environment.
Continuous Self-Rewarding Process
A learning mechanism where agents receive feedback based on their performance, allowing them to improve autonomously.
D-GARA
D-GARA is a dynamic benchmarking framework for evaluating the robustness of GUI agents against real-world anomalies.
Continuous Control
A type of reinforcement learning task where the agent must make decisions in a continuous action space, often used in robotics and simulation environments.
Reward Profiling
A method for selectively updating the policy based on high-confidence performance estimations to improve the stability and convergence of reinforcement learning algorithms.
Policy Gradient
A class of algorithms in reinforcement learning that optimize the policy directly by adjusting the parameters in the direction of the gradient of expected reward.
Reinforcement Learning
A type of machine learning where an agent learns to make decisions by receiving rewards or penalties based on its actions in an environment.