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…ces to decouple pricing agent from the operator class.
This commit implements a comprehensive gym-like environment system that completely decouples RL agents from the EVCH simulation, enabling easy integration with any gym-compatible RL library. Key Features: - Created unified EVCHGymEnv with standard gym interface (reset, step, render) - Built gym agent adapters for pricing, charging, and storage agents - Enhanced factory functions for easy gym agent integration - Added comprehensive examples and documentation New Files: - utilities/rl_environments/evch_gym_env.py: Unified gym environment wrapper - utilities/rl_agents/adapters/gym_agent_adapter.py: Gym agent adapters - examples/gym_agent_integration_example.py: Working examples - docs/gym_integration_guide.md: Comprehensive integration guide Modified Files: - utilities/rl_agents/factory.py: Added gym agent factory functions Benefits: - Complete decoupling of RL agents from simulation - Standard gym interface for any RL library (Stable Baselines3, RLlib, etc.) - Easy plug-and-play agent swapping - Support for both continuous and discrete action spaces - Scalable architecture for distributed training The simulation now works exactly like standard gym environments, allowing seamless integration with any gym-compatible RL agent without modifying simulation code.
Dependency Review✅ No vulnerabilities or license issues or OpenSSF Scorecard issues found.Scanned FilesNone |
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