Update README.md

README.md

@@ -1,6 +1,69 @@
 ---
 license: apache-2.0
+---
+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
+
+## 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