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--- |
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tags: |
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- reinforcement-learning |
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- openenv |
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- environment |
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- gymnasium |
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license: mit |
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library_name: openenv |
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--- |
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<h1 align="center">⚡ EVChargeEnv</h1> |
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<p align="center"> |
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<img src="assets/evchargeenv-banner.png" width="800" /> |
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</p> |
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<h3 align="center">Green Agent Benchmark for EV Charging Optimization</h3> |
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--- |
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## Overview |
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EVChargeEnv is a lightweight, stochastic reinforcement-learning environment designed for the |
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AgentX + AgentBeats Competition (Berkeley RDI 2025). |
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It simulates: |
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- Electric vehicle battery charging |
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- Dynamic electricity pricing |
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- Fluctuating grid load |
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- Continuous control actions |
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- Multi-objective tradeoffs (cost vs. speed vs. grid stability) |
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--- |
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## Task Goal |
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The purple agent must: |
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- Charge the EV battery to full (1.0) |
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- Minimize electricity cost |
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- Avoid high grid load |
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- Adapt to changing conditions |
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--- |
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## State Space (Observation) |
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The agent receives: |
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charge_level (0-1), price (0-1), grid_load (0-1), time_step_norm (0-1) |
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--- |
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## Action Space |
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Continuous charge rate 0.0 → 1.0. |
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--- |
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## Reward Function |
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Reward combines: |
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- progress_reward |
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* cost_penalty |
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* overload_penalty |
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* time_penalty |
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--- |
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## Scenarios |
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easy / medium / hard difficulty with different volatility and load patterns. |
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--- |
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## Episode Termination |
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Ends if full charge or max steps reached. |
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--- |
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## Example Agent Behaviors |
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Greedy agent = fast but expensive |
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Price-aware agent = slower but cheaper |
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Random agent = unstable |
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--- |
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## Evaluation Output |
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Running: |
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python run_evaluation.py |
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Generates JSON like: |
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{ |
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"avg_reward": ..., |
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"avg_steps": ..., |
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"episodes": 5 |
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} |
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--- |
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## Docker Support |
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Image: oozan/evchargeenv:latest |
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--- |
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## File Structure |
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env/ |
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agent/ |
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run_evaluation.py |
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Dockerfile |
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requirements.txt |
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README.md |
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--- |
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## Future Improvements |
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- renewable energy factor |
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- blackout events |
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- degradation model |
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- RL baseline |
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- trajectory visualizer |
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- mini-game UI |
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## Benchmark Specification |
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This repository also includes a machine-readable benchmark manifest: |
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- `evchargeenv_manifest.json` |
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It documents: |
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- state and action spaces |
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- reward components |
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- termination conditions |
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- supported scenarios (`easy`, `medium`, `hard`) |
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- evaluation output format (JSON fields) |
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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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'' |
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