Reinforcement Learning for Long-Horizon Unordered Tasks: From Boolean to Coupled Reward Machines

1. 1Implement reward machines to inform reinforcement learning agents about the environment's reward structure
2. 2Apply Boolean to coupled reward machines to handle long-horizon unordered tasks
3. 3Configure the reinforcement learning algorithm to handle non-Markovian tasks
4. 4Test the approach on a variety of tasks with increasing complexity
5. 5Analyze the results to evaluate the improvement in sample efficiency and task completion

AI engineers and researchers can benefit from this approach to tackle complex tasks, while data scientists can apply these methods to real-world problems

💡 Reward machines can improve sample efficiency and task completion in reinforcement learning, especially for non-Markovian tasks

💡 Reinforcement learning for long-horizon unordered tasks just got a boost with reward machines!