Projects
Common bio-inspired algorithms (Genetic Algorithm, Multi-Objective Genetic Algorithm with Pareto Ranking, and Particle Swarm Optimization) implemented in Python
Common bio-inspired algorithms (Genetic Algorithm, Multi-Objective Genetic Algorithm with Pareto Ranking, and Particle Swarm Optimization) implemented in Python
Common bio-inspired algorithms (Genetic Algorithm, Multi-Objective Genetic Algorithm with Pareto Ranking, and Particle Swarm Optimization) implemented in Python
Common bio-inspired algorithms (Genetic Algorithm, Multi-Objective Genetic Algorithm with Pareto Ranking, and Particle Swarm Optimization) implemented in Python
Common bio-inspired algorithms (Genetic Algorithm, Multi-Objective Genetic Algorithm with Pareto Ranking, and Particle Swarm Optimization) implemented in Python
Common bio-inspired algorithms (Genetic Algorithm, Multi-Objective Genetic Algorithm with Pareto Ranking, and Particle Swarm Optimization) implemented in Python
MA-CDL
Signal8
CARLA AEBS
Advanced Emergency Braking System (AEBS) implemented with PyTorch, using the CARLA simulator
RL Toolkit
Common reinforcement learning algorithms (Q-Table, DQN, Dueling DQN, REINFORCE, Advantage Actor-Critic) evaluated on OpenAI Gym's CartPole environment using PyTorch
GEAT
Common bio-inspired algorithms (Genetic Algorithm, Multi-Objective Genetic Algorithm with Pareto Ranking, and Particle Swarm Optimization) implemented in Python
Face Verification
Facial verification system implemented in TensorFlow, using transfer learning with pre-trained Xception model
Advanced Emergency Braking System (AEBS) implemented with PyTorch, using the CARLA simulator
Advanced Emergency Braking System (AEBS) implemented with PyTorch, using the CARLA simulator
Advanced Emergency Braking System (AEBS) implemented with PyTorch, using the CARLA simulator
Advanced Emergency Braking System (AEBS) implemented with PyTorch, using the CARLA simulator
Advanced Emergency Braking System (AEBS) implemented with PyTorch, using the CARLA simulator
Advanced Emergency Braking System (AEBS) implemented with PyTorch, using the CARLA simulator
Advanced Emergency Braking System (AEBS) implemented with PyTorch, using the CARLA simulator
Advanced Emergency Braking System (AEBS) implemented with PyTorch, using the CARLA simulator
Advanced Emergency Braking System (AEBS) implemented with PyTorch, using the CARLA simulator
Advanced Emergency Braking System (AEBS) implemented with PyTorch, using the CARLA simulator
Advanced Emergency Braking System (AEBS) implemented with PyTorch, using the CARLA simulator
Advanced Emergency Braking System (AEBS) implemented with PyTorch, using the CARLA simulator
Advanced Emergency Braking System (AEBS) implemented with PyTorch, using the CARLA simulator
Advanced Emergency Braking System (AEBS) implemented with PyTorch, using the CARLA simulator
Common bio-inspired algorithms (Genetic Algorithm, Multi-Objective Genetic Algorithm with Pareto Ranking, and Particle Swarm Optimization) implemented in Python
Common bio-inspired algorithms (Genetic Algorithm, Multi-Objective Genetic Algorithm with Pareto Ranking, and Particle Swarm Optimization) implemented in Python
Common bio-inspired algorithms (Genetic Algorithm, Multi-Objective Genetic Algorithm with Pareto Ranking, and Particle Swarm Optimization) implemented in Python
Common bio-inspired algorithms (Genetic Algorithm, Multi-Objective Genetic Algorithm with Pareto Ranking, and Particle Swarm Optimization) implemented in Python
Common bio-inspired algorithms (Genetic Algorithm, Multi-Objective Genetic Algorithm with Pareto Ranking, and Particle Swarm Optimization) implemented in Python
Common bio-inspired algorithms (Genetic Algorithm, Multi-Objective Genetic Algorithm with Pareto Ranking, and Particle Swarm Optimization) implemented in Python
By implementing Commutative RL, we enable agents to autonomously develop a grounded and emergent coordination language for distributed communication across a host of cooperative tasks.
A 2D multi-agent navigation domain that requires inter-agent communication for successful path planning.
Companion Bot
Built a small, wheeled robot with ultrasonic sensors that can follow a moving target, using SLAM and a PID controller.
Demonstrated how our method of Commutative Reinforcement Learning is able to outperform traditional reinforcement learning in problems where the agent can modify its environment (transition & reward function) to receive an even greater reward.
Blocksworld3D brings the classic blocksworld planning problem to a 3D domain, providing eight unique problem instances accessible through a graphical user interface
Advanced Emergency Braking System (AEBS) implemented in PyTorch, using the CARLA driving simulator.
Collection of reinforcement learning algorithms (i.e., Q-Table, DQN, Dueling DQN, REINFORCE, Advantage Actor-Critic) evaluated on OpenAI Gym's CartPole environment using PyTorch.
Collection of evolutionary algorithms (i.e., Genetic Algorithm, Multi-Objective Genetic Algorithm with Pareto Ranking, and Particle Swarm Optimization) implemented in Python.