| --- |
| license: apache-2.0 |
| pipeline_tag: reinforcement-learning |
| tags: |
| - rlgym |
| - rocket-league |
| - RLBot |
| - PPO |
| --- |
| |
| # 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 |
|
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|
|
| ## 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 |
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|
