## Session Summary [00:00:21 ] (auto-generated)
**Agent Profile: Multi-Agent RL Training System**
*   **Role:** Multi-Agent Reinforcement Learning Agent
*   **Primary Objective:** Train agents to navigate complex environments (Maze, Hide & Seek) while managing energy and maximizing score.
*   **Core Mechanisms:**
    *   **Physics Engine:** Uses PyMunk for rigid body dynamics.
    *   **Reward System:** Dynamic rewards based on energy conservation, score, and survival.
    *   **Environment:** Customized to handle different modes (Maze, Hide & Seek) with specific reward functions.

**Key Decisions Made:**
*   **Collision Resolution:** Fixed object collision handling in `add_object()` by explicitly setting `collision_type = COLLISION_TYPE_OBJECT` on all newly created shapes. Previously, agents passed through objects due to missing collision definitions.
*   **Agent Initialization:** Modified `add_agent()` to automatically create a physics body if the simulation is already running, preventing agents from sitting at (0,0) with no movement.
*   **Long-Running Stability:** Implemented signal handling to ensure RL training does not crash or timeout on service restarts, with automatic re-entrancy.
*   **Energy Management:** Integrated energy recovery logic into the reward function for Hide & Seek agents, allowing them to sustain while hiding.

**Completed vs. In Progress:**
*   **Completed:**
    *   Fixed collision types for all UI-placed objects (boxes, ramps, crates, barrels).
    *   Implemented signal handling for long-running training stability.
    *   Configured energy recovery rewards for Hide & Seek modes.
    *   Verified agent placement logic at (13,82).
*   **In Progress:**
    *   Debugging specific object collision types in Hide & Seek mode.
    *   Finalizing the "Long Run" feature configuration.

**Next Step:**
*   **Execute:** Run the training loop with the new collision and energy recovery configurations.
*   **Verify:** Check agent performance metrics (score, energy, survival) and confirm no crashes during long training sessions.