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+EVChargeEnv: An OpenEnv Benchmark for EV Charging Optimization
+
+1. Motivation
+
+As AI agents move from static prediction to acting autonomously in dynamic environments, we need richer environments than toy grids and games. One domain that naturally combines uncertainty, long-horizon planning, and multi-objective decision-making is electric vehicle (EV) charging.
+
+EVChargeEnv is my contribution to the OpenEnv Challenge. It simulates a simplified but realistic EV charging process where an agent must decide how much to charge at each timestep while adapting to fluctuating electricity prices and grid load.
+
+The core objective is:
+
+Reach full battery while minimizing cost and avoiding grid overload.
+
+This makes EVChargeEnv a clean and interpretable environment that still contains meaningful complexity.
+
+---
+
+2. Environment Design
+
+EVChargeEnv exposes a continuous-control RL task with a 4-dimensional state:
+
+- charge_level ∈ [0, 1] – battery state of charge
+- price ∈ [0, 1] – dynamic energy price
+- grid_load ∈ [0, 1] – current grid stress/instability
+- time_step_norm ∈ [0, 1] – normalized timestep
+
+Agents output a continuous charging rate between 0 and 1.
+
+Scenarios
+
+To test robustness, EVChargeEnv includes three difficulty modes:
+
+- easy – smooth price/load curves and short episodes
+- medium – balanced volatility (default scenario)
+- hard – noisy price/load dynamics and slower charging
+
+---
+
+3. Reward Function
+
+The reward balances several competing factors:
+
+- progress_reward – reward for increasing charge
+  – cost_penalty – charging during high prices costs more
+  – overload_penalty – charging when grid load is high is discouraged
+  – time_penalty – each step costs a tiny penalty to encourage faster execution
+
+This encourages agents to:
+
+- charge aggressively during low-price, low-load periods
+- slow or stop charging during peak price/load
+- finish charging efficiently
+
+---
+
+4. Implementation and OpenEnv Integration
+
+The environment is implemented using gymnasium and structured to reflect OpenEnv specifications. Key files include:
+
+- env/ev_charge_env.py – main environment logic
+- openenv.yaml – metadata describing observation/action spaces, rewards, and termination criteria
+- run_evaluation.py – produces standardized JSON outputs for assessment
+
+Example output:
+
+{
+"avg_reward": -1.23,
+"avg_steps": 31.2,
+"episodes": 5
+}
+
+Published on the Hugging Face Hub:
+https://huggingface.co/oozan/EVChargeEnv
+
+---
+
+5. Baseline Agents and Training
+
+Baselines included:
+
+Random Baseline – ignores price/load.
+Price-Aware Baseline – charges more when price is low.
+PyTorch Policy-Gradient Agent – small neural model trained with REINFORCE.
+
+The learned agent shows:
+
+- improved reward
+- sensible patterns
+- adaptation to medium scenario
+
+---
+
+6. Running the Environment
+
+Install:
+
+pip install -r requirements.txt
+
+Evaluate:
+
+python run_evaluation.py
+
+Heuristic baseline:
+
+python run_price_aware_evaluation.py
+
+Train agent:
+
+python train_evchargeenv_pg.py
+
+Notebook also included for Colab execution.
+
+---
+
+7. Future Improvements
+
+Possible extensions:
+
+- Day/night pricing cycles
+- Renewable energy influence
+- Emergency events / blackouts
+- Battery degradation modeling
+- PPO/SAC/LLM-based agents
+- Visualization tools
+
+---
+
+Final Thoughts
+
+EVChargeEnv is:
+
+- simple
+- realistic
+- modular
+- OpenEnv-compliant
+
+It provides a practical environment for research on planning and resource optimization under uncertainty.
+
+Repo:
+https://huggingface.co/oozan/EVChargeEnv