• Reviewed existing RL studies in sports.

• Identified gaps: small datasets, poor real-world validation, low adaptability.

• Use real sports data and simulated environments (OpenAI Gym / Unity ML-Agents).

• Apply data augmentation to overcome limited datasets.

• Implement advanced RL algorithms: DQN, PPO, Actor-Critic.

• Integrate deep learning (CNN/RNN) for spatial & temporal understanding.

• Design rewards to reflect multiple objectives (scoring, defence, energy efficiency).

• Provide intermediate feedback, adapt to task difficulty.

• Techniques: Inverse RL, Reward Shaping, Curriculum Learning.

• Training with real-time and varied-quality video data.

• Use YOLOv8 + DeepSORT to extract positions and trajectories in real time.

• Optimize learning via Transfer Learning and Hyperparameter Tuning.

• Compare RL models with traditional methods.

• Techniques: A/B Testing, Monte Carlo Tree Search (MCTS).