A Comparative Study of Reinforcement Learning Algorithms Under Task Performance and Energy Constraints with Transfer Learning Analysis
Three widely used RL algorithms for continuous robotic control are compared, finding that A2C exhibited highly unstable performance under energy-aware rewards on MountainCarContinuous-v0, suggesting that A2C may require additional reward shaping or tuning before use in energy-constrained robotic applications.