Dynamic Reward Shaping for Robotic Manipulation: A Self-Adjusting Mechanism Based on Task Complexity in Deep Reinforcement Learning
Year: 2025
Type: conference-paper
Abstract: This paper presents a novel dynamic reward shaping framework for robotic manipulation tasks using deep reinforcement learning. Unlike traditional reward shaping methods, which use static functions, this approach adapts the reward structure in real-time based on task complexity indicators such as success rates, trajectory lengths, and exploration diversity. By modeling task complexity with Beta distributions, the system shifts from exploratory to exploitative learning as the agent becomes more proficient. The proposed mechanism enhances learning efficiency, stability, and scalability in high-dimensional robotic environments.
Authors: Lareb, Shariya, Ravula, Sheethal
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6949643297
FWCI:
Cites:
Cited by:
Related to: