# Anticipatory Reinforcement Learning for Trajectory Tracking

arXiv: https://arxiv.org/abs/2607.03132 · HTML: https://mlsanity.com/paper/2607.03132/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 4 of its 6 references that are in the map.
- First posted on arXiv 3 Jul 2026.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 4 of 4)

- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Overcoming the Sim-to-Real Gap: Leveraging Simulation to Learn to Explore for Real-World RL](https://mlsanity.com/paper/2410.20254/) (2024)
- [Python-Based Reinforcement Learning on Simulink Models](https://mlsanity.com/paper/2405.08567/) (2024)
- [The Crucial Role of Problem Formulation in Real-World Reinforcement Learning](https://mlsanity.com/paper/2503.20442/) (2025)

## Abstract (arXiv)

Deep reinforcement learning (DRL) in industrial control often suffers from lag and overshoot due to purely reactive control based on the current tracking error. To achieve anticipatory control without high computational overhead, we introduce a predictive formulation that augments the DRL state space with target velocities and future reference horizons. Evaluating eight configurations using proximal policy optimization (PPO) on a 1-degree-of-freedom (1-DoF) helicopter testbed, simulation results showed a 9-fold error reduction, lowering the mean absolute deviation from 2.73deg to 0.31deg. However, zero-shot transfer to physical hardware revealed a sim-to-real gap. Interestingly, a simpler configuration using a single, further look-ahead horizon matched the real-world top performance of the most complex model (1.11deg). Overall, evaluating various combinations of prediction horizons and target velocities demonstrated that highly granular predictive data is not necessarily required for physical transfer.