# Continuous-action Reinforcement Learning for Playing Racing Games: Comparing SPG to PPO

arXiv: https://arxiv.org/abs/2001.05270 · HTML: https://mlsanity.com/paper/2001.05270/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jan 2020.

## Built on by (most cited first, 4 of 4)

- [Generalized Proximal Policy Optimization with Sample Reuse](https://mlsanity.com/paper/2111.00072/) (2021)
- [Sample Dropout: A Simple yet Effective Variance Reduction Technique in Deep Policy Optimization](https://mlsanity.com/paper/2302.02299/) (2023)
- [RAMario: Experimental Approach to Reptile Algorithm -- Reinforcement Learning for Mario](https://mlsanity.com/paper/2305.09655/) (2023)
- [Fine-tuning Timeseries Predictors Using Reinforcement Learning](https://mlsanity.com/paper/2603.20063/) (2026)

## Abstract (arXiv)

In this paper, a novel racing environment for OpenAI Gym is introduced. This environment operates with continuous action- and state-spaces and requires agents to learn to control the acceleration and steering of a car while navigating a randomly generated racetrack. Different versions of two actor-critic learning algorithms are tested on this environment: Sampled Policy Gradient (SPG) and Proximal Policy Optimization (PPO). An extension of SPG is introduced that aims to improve learning performance by weighting action samples during the policy update step. The effect of using experience replay (ER) is also investigated. To this end, a modification to PPO is introduced that allows for training using old action samples by optimizing the actor in log space. Finally, a new technique for performing ER is tested that aims to improve learning speed without sacrificing performance by splitting the training into two parts, whereby networks are first trained using state transitions from the replay buffer, and then using only recent experiences. The results indicate that experience replay is not beneficial to PPO in continuous action spaces. The training of SPG seems to be more stable when actions are weighted. All versions of SPG outperform PPO when ER is used. The ER trick is effective at improving training speed on a computationally less intensive version of SPG.