RL without TD learning

1. 1Understand the limitations of traditional temporal difference learning in reinforcement learning
2. 2Learn about the divide and conquer approach and its potential to scale to long-horizon tasks
3. 3Explore the application of this approach in off-policy reinforcement learning
4. 4Investigate the use of this approach in domains where data collection is expensive, such as robotics and healthcare

Researchers and engineers working on reinforcement learning and robotics can benefit from this approach as it provides a new paradigm for value learning that can scale to complex, long-horizon tasks

💡 The divide and conquer approach provides a fundamentally different way to solve the error accumulation problem in value learning

💡 Reinforcement learning without TD learning: a new divide and conquer approach