Reinforcement learning via conservative agent for environments with random delays

Key Points

• Improved policy optimization performance is achieved using a conservative agent approach under random delays.
• The conservative agent model shows a significant increase in adaptability, performing 25% better in test scenarios involving random delays.
• Observational analysis across diverse environments reveals that traditional reinforcement learning struggles with unpredictable delays.
• These findings highlight the potential for advanced reinforcement learning techniques to enhance AI adaptability in real-world applications.