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Multi-Agent Market Simulation
A complex simulation environment for training Reinforcement Learning agents in market dynamics, featuring promoter roles and behavioral experiments.
The Problem
Understanding emergent market behaviors and agent interactions in mixed-incentive environments requires complex simulation capabilities not found in standard RL environments.
The Solution
Implemented a multi-agent environment where RL agents compete and cooperate. Includes a "promoter" role to influence market dynamics and tools to analyze behavioral shifts.
System Architecture
Custom OpenAI Gym environment with multi-agent support. Agents are trained using PPO (Proximal Policy Optimization) and evaluated on cooperative metrics.
Architecture Diagram Placeholder
Tech Stack
PythonPyTorchOpenAI GymPandasSeaborn
ML Performance
Model Architecture
Multi-Agent PPO
Inference Latency
2ms
Key Metrics
15.4
average Reward
0.05
nash Equilibrium Dist