# Generation of Geodesics with Actor-Critic Reinforcement Learning to Predict Midpoints

arXiv: https://arxiv.org/abs/2407.01991 · HTML: https://mlsanity.com/paper/2407.01991/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL theory and sample complexity, one of 15 directions in Deep reinforcement learning methods.
- Builds on 12 of its 48 references that are in the map.
- First posted on arXiv 2 Jul 2024.

## Placed in

- Deep reinforcement learning methods / [RL theory and sample complexity](https://mlsanity.com/deeprl/rl-theory-and-sample-complexity/)

## Builds on (most cited first, 12 of 12)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://mlsanity.com/paper/1603.04467/) (2016)
- [Optimal Goal-Reaching Reinforcement Learning via Quasimetric Learning](https://mlsanity.com/paper/2304.01203/) (2023)
- [Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors](https://mlsanity.com/paper/2006.13205/) (2020)
- [Is Long Horizon Reinforcement Learning More Difficult Than Short Horizon Reinforcement Learning?](https://mlsanity.com/paper/2005.00527/) (2020)
- [Sub-Goal Trees -- a Framework for Goal-Based Reinforcement Learning](https://mlsanity.com/paper/2002.12361/) (2020)
- [Divide-and-Conquer Monte Carlo Tree Search For Goal-Directed Planning](https://mlsanity.com/paper/2004.11410/) (2020)
- [Floyd-Warshall Reinforcement Learning: Learning from Past Experiences to Reach New Goals](https://mlsanity.com/paper/1809.09318/) (2018)
- [On Linear Interpolation in the Latent Space of Deep Generative Models](https://mlsanity.com/paper/2105.03663/) (2021)
- [A Reinforcement Learning based Path Planning Approach in 3D Environment](https://mlsanity.com/paper/2105.10342/) (2021)

## Built on by (most cited first, 1 of 1)

- [Hierarchical Soft Actor-Critic for Sparse-Reward Long-Horizon Reinforcement Learning](https://mlsanity.com/paper/2607.23726/) (2026)

## Abstract (arXiv)

To find the shortest paths for all pairs on manifolds with infinitesimally defined metrics, we introduce a framework to generate them by predicting midpoints recursively. To learn midpoint prediction, we propose an actor-critic approach. We prove the soundness of our approach and show experimentally that the proposed method outperforms existing methods on several planning tasks, including path planning for agents with complex kinematics and motion planning for multi-degree-of-freedom robot arms.