⚡ EVChargeEnv
Green Agent Benchmark for EV Charging Optimization
Overview
EVChargeEnv is a lightweight, stochastic reinforcement-learning environment designed for the
AgentX + AgentBeats Competition (Berkeley RDI 2025).
It simulates:
- Electric vehicle battery charging
- Dynamic electricity pricing
- Fluctuating grid load
- Continuous control actions
- Multi-objective tradeoffs (cost vs. speed vs. grid stability)
Task Goal
The purple agent must:
- Charge the EV battery to full (1.0)
- Minimize electricity cost
- Avoid high grid load
- Adapt to changing conditions
State Space (Observation)
The agent receives:
charge_level (0-1), price (0-1), grid_load (0-1), time_step_norm (0-1)
Action Space
Continuous charge rate 0.0 → 1.0.
Reward Function
Reward combines:
- cost_penalty
- overload_penalty
- time_penalty
Scenarios
easy / medium / hard difficulty with different volatility and load patterns.
Episode Termination
Ends if full charge or max steps reached.
Example Agent Behaviors
Greedy agent = fast but expensive
Price-aware agent = slower but cheaper
Random agent = unstable
Evaluation Output
Running:
python run_evaluation.py
Generates JSON like:
{
"avg_reward": ...,
"avg_steps": ...,
"episodes": 5
}
Docker Support
Image: oozan/evchargeenv:latest
File Structure
env/
agent/
run_evaluation.py
Dockerfile
requirements.txt
README.md
Future Improvements
- renewable energy factor
- blackout events
- degradation model
- RL baseline
- trajectory visualizer
- mini-game UI
Benchmark Specification
This repository also includes a machine-readable benchmark manifest:
evchargeenv_manifest.json
It documents:
- state and action spaces
- reward components
- termination conditions
- supported scenarios (
easy, medium, hard)
- evaluation output format (JSON fields)
This makes EVChargeEnv easier to integrate as a standardized benchmark and aligns with the spirit of the OpenEnv challenge: environments that are transparent, reproducible, and extensible.
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