Delayed Homomorphic Reinforcement Learning for Environments with Delayed Feedback

1. 1Identify environments with delayed feedback and the resulting state-space explosion
2. 2Apply canonical state augmentation approaches and assess their limitations
3. 3Implement delayed homomorphic reinforcement learning to address the Markov assumption and sample-complexity burden
4. 4Evaluate the performance of the proposed approach in various environments and scenarios

This research benefits AI engineers and ML researchers working on reinforcement learning and control systems, as it provides a novel approach to handling delayed feedback in real-world environments

💡 Delayed homomorphic reinforcement learning can effectively handle environments with delayed feedback, reducing the state-space explosion and improving learning and control

🤖 New approach to reinforcement learning: delayed homomorphic RL for environments with delayed feedback! 📊