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The CoachAI Badminton Environment: A Novel Reinforcement Learning Environment with Realistic Opponents (Student Abstract)
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Proposes the CoachAI Badminton Environment, an open-source turn-based RL environment whose opponents are driven by ShuttleNet, DyMF and a behavior cloning model trained on real match records, and benchmarks DDPG, A2C and PPO against them.
Problem
Existing sports RL environments use simple rule-based opponents, creating performance gaps when strategies are validated against real matches; turn-based sports like badminton lack environments with realistic opponents.
Approach
Model opponent actions as transition dynamics using pretrained tactical-forecasting models (ShuttleNet for shot type and landing, DyMF for movement) combined into weighted bivariate Gaussian outputs, with a behavior cloning model for the first two strokes when history is insufficient.
Key results
- Integrated ShuttleNet+DyMF approach achieves shot CE 1.8442, landing MAE 1.4060, moving MAE 1.6007, outperforming standalone ShuttleNet (shot 2.4699, land 1.4276) and DyMF (shot 2.1094, move 1.6699).
- BC model for first two strokes achieves shot CE 1.0662, landing MAE 1.3647, moving MAE 0.8770.
- A2C only outperforms Opponent B after 700K training steps and struggles against Opponent A; A2C relies on Clear, Drive, Defensive Shot while PPO shows more even shot distribution.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Integrate ShuttleNet and DyMF instead of using ShuttleNet alone | Opponent AI prediction · Future-stroke prediction | cross-entropy (shot type): 2.4699 -> 1.8442 | Helped |
| Integrate ShuttleNet and DyMF instead of using ShuttleNet alone | Opponent AI prediction · Future-stroke prediction | MAE (landing position): 1.4276 -> 1.4060 | Helped |
| Integrate ShuttleNet and DyMF instead of using DyMF alone | Opponent AI prediction · Future-stroke prediction | cross-entropy (shot type): 2.1094 -> 1.8442 | Helped |
| Integrate ShuttleNet and DyMF instead of using DyMF alone | Opponent AI prediction · Future-stroke prediction | MAE (moving position): 1.6699 -> 1.6007 | Helped |
Surprising findings
- A2C only beats Opponent B after 700K steps and cannot compete against Opponent A, showing opponent choice strongly affects results.
Limitations
- Benchmark results are preliminary, evaluated on only two real-world opponents over 20 sets.
- Opponent models only cover singles badminton with a fixed set of 12 shot types.
Experimental setup
Datasets: real badminton game records
Benchmarks: Badminton Benchmarks
Metrics: average score difference mean absolute error (MAE) cross-entropy (CE)
Baselines: ShuttleNet DyMF DDPG A2C PPO
Builds on: ShuttleNet DyMF Behavior Cloning Proximal Policy Optimization Google Research Football
Closest papers by meaning
- The CoachAI Badminton Environment: Bridging the Gap between a Reinforcement Learning Environment and Real-World Badminton Games (AAAI 2024 · similarity 0.96)
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- LATTE-MV: Learning to Anticipate Table Tennis Hits from Monocular Videos (CVPR 2025 · similarity 0.83)
- Gymnasium: A Standard Interface for Reinforcement Learning Environments (NeurIPS 2025 · similarity 0.83)
- SportR: A Benchmark for Multimodal Large Language Model Reasoning in Sports (ICLR 2026 · similarity 0.83)
- FightLadder: A Benchmark for Competitive Multi-Agent Reinforcement Learning (ICML 2024 · similarity 0.83)
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