Luis Lopez
Washington DC-Baltimore Area United States
Summary
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Rockstar🎓
Top SchoolLuis Lopez is an electrical and software engineer with 12 years of experience designing and implementing critical power systems and building full-stack software solutions. He blends hardware-focused engineering from a BE in Electrical and Electronics Engineering with practical software contributions, notably integrating RLCard games into the popular PettingZoo multi-agent RL framework. Comfortable working across system boundaries, he adapts observation, action, and reward spaces to make complex simulations interoperable and testable. Based in the Washington DC–Baltimore area, he brings a pragmatic, systems-oriented mindset to projects that require both circuit-level insight and robust software engineering.
12 years of coding experience
Bachelor of Engineering - BE, Electrical and Electronics Engineering, Bachelor of Engineering - BE, Electrical and Electronics Engineering at University of Maryland - A. James Clark School of EngineeringEngineering Science - AS, Electrical and Electronics Engineering, Engineering Science - AS, Electrical and Electronics Engineering at Montgomery College
Spanish, English
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (10)
gymnasium10
openai-gym10
python10
reinforcement-learning10
enviroment9
multi-agent-reinforcement-learning9
environ9
dev-environment9
api9
environmental9
Programming languages (2)
Github contributions (5)
Farama-Foundation/PettingZoo
Mar 2020 - Jan 2021
An API standard for multi-agent reinforcement learning environments, with popular reference environments and related utilities
Role in this project:
Full-stack Developer
Full-stack DeveloperContributions:53 commits, 5 PRs, 12 pushes in 10 months
Contributions summary:Luis's commits focused on integrating RLCard games into the pettingzoo environment. This involved creating new environments by wrapping RLCard games like Uno, Texas Hold'em, Leduc Hold'em, and Mahjong within the PettingZoo framework. Additionally, the commits updated existing tests and documentation to ensure compatibility and proper functionality of the integrated RLCard games. These changes involved adapting observation spaces, action spaces, and reward functions to align with the PettingZoo's environment requirements.
agentreinforcement-learningreinforcement-learning-agentgymnasiumdeep-reinforcement-learning
lssr/sofa
Nov 2019 - Jan 2022
Structural Optimization with FEniCS and dolfin-Adjoint
Contributions:1 PR, 5 pushes in 2 years 1 month
fenicsoptimizationdolfin-adjointadjointstructural
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