Reinforcement Learning Achieves Near-Perfect Win Rates in Simulated Tennis via Curriculum Learning

Global: Reinforcement Learning Achieves Near-Perfect Win Rates in Simulated Tennis via Curriculum Learning

A new research paper posted on arXiv introduces a reinforcement learning framework that enables an artificial agent to win between 98 % and 100 % of matches in a detailed tennis simulation. The study, authored by a single researcher, combines a custom environment with a Dueling Double Deep Q‑Network (DDQN) and curriculum learning to address the sport’s hierarchical scoring, stochastic outcomes, fatigue dynamics, and opponent skill adaptation.

Simulation Environment

The environment replicates full tennis scoring at the point, game, and set levels while modeling rally‑level tactical decisions across ten discrete action categories. It incorporates symmetric fatigue effects for both players and a continuous parameter that governs opponent skill, allowing the agent to experience realistic long‑horizon credit assignment challenges.

Learning Architecture

The agent employs a dueling network architecture that separates state‑value estimation from action‑specific advantages, and double Q‑learning to mitigate overestimation bias. These design choices aim to improve training stability in the stochastic, long‑horizon domain of tennis.

Curriculum Learning Approach

Curriculum learning progressively raises opponent difficulty from a skill level of 0.40 to 0.50. This staged increase enables the agent to acquire robust strategies without the training collapse observed when facing a fixed‑difficulty opponent throughout learning.

Performance Outcomes

Extensive evaluations report win rates ranging from 98 % to 100 % against balanced opponents. Serve efficiency falls between 63.0 % and 67.5 %, while return efficiency varies from 52.8 % to 57.1 %. These metrics indicate strong overall performance across key aspects of tennis play.

Ablation and Policy Analysis

Ablation studies confirm that both the dueling architecture and curriculum learning are essential for stable convergence; a standard DQN baseline fails to learn effective policies. However, tactical analysis reveals a pronounced defensive bias in the learned policy, with the agent favoring error avoidance and prolonged rallies over aggressive point construction.

Implications for Future Research

The findings highlight a limitation of win‑rate‑driven optimization in simplified sports simulations and suggest that more nuanced reward designs are needed to encourage realistic, offensive strategies. Researchers are encouraged to explore alternative objectives and richer simulation dynamics to better capture the complexity of real‑world tennis.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.