CanoPy

CanoPy is a self-playing reinforcement learning Rocket League agent designed for the RLBot Championship 2025.
It uses PPO (Proximal Policy Optimization) to learn 2v2 gameplay through self-play. The agent is trained to play effectively on both blue and orange teams and can generalize to various team compositions.

Model Details

• Framework: RLGym + RLBot v5
• Algorithm: PPO (via rlgym-ppo)
• Team size: 2v2
• Action repeat: 8
• Observations: DefaultObs with normalized positions, angles, velocities, and boost
• Action space: Lookup table actions with repeat frames
• Reward shaping: Combined reward including:
  • Speed toward ball
  • In-air bonus
  • Ball velocity toward goal
  • Goal scoring reward

Training Configuration (from config.json)

• Number of processes: 4
• Minimum inference ratio: 80%
• Steps per checkpoint: 1,000,000
• PPO batch size: 100,000
• PPO minibatch size: 50,000
• PPO epochs per update: 2
• Experience buffer size: 300,000
• Policy network layers: [256, 128]
• Critic network layers: [256, 128]
• Policy learning rate: 0.0001
• Critic learning rate: 0.0001
• PPO entropy coefficient: 0.01
• Standardize returns: true
• Standardize observations: false
• Total training steps: 1,000,000,000
• Checkpoint directory: ./checkpoints

Intended Use

CanoPy is intended for research, competition, and experimentation within the RLBot framework. It is designed to compete in the ML bot bracket of the RLBot Championship 2025.

Limitations

• Performance is dependent on training; untrained or partially trained models may perform poorly.
• The bot has been trained for standard Rocket League 2v2 matches; it may not generalize to unusual map sizes, mutators, or game modes.
• Does not include human-like strategy beyond what PPO has learned from self-play.

Evaluation

CanoPy can be evaluated using the evaluate() function in the training script. Expected evaluation includes average episode returns and gameplay against copies of itself.

• Note: To meet RLBot Championship submission requirements, further testing against Psyonix Pro bots may be necessary.

Contact / Author

• Author: FlameF0X /// Discord handler @flame_f0x
• Competition: RLBot Championship 2025