Hackathon Round 1: AgentX: The Self-Learning Agent Challenge
Problem Statement 1: Build an RL-Powered Adaptive AgentDesign AgentX, an autonomous AI agent that learns optimal behavior through Reinforcement Learning.
The agent must:
Observe its environment
Take actions
Learn from rewards or penalties
Improve performance over time without manual reprogramming
Your task:
Create a system where AgentX continuously adapts to changing conditions (e.g., user behavior, system state, game environment) and demonstrates measurable improvement in efficiency, accuracy, or decision-making.
Problem Statement 2: Gamified Learning Environment for AgentXBuild a gamified environment or simulation where AgentX can train, evolve, and demonstrate intelligent behavior.
Examples include:
Puzzle-solving worlds
Resource-optimization challenges
Maze navigation
User-engagement gamification platforms
Your task:
Design the game mechanics, reward structure, and performance metrics. AgentX should be capable of exploring, learning strategies, and achieving higher scores or faster completion times as training progresses.
Problem Statement 3: Multi-Agent RL Collaboration or CompetitionExtend AgentX into a multi-agent ecosystem, where multiple RL-driven agents:
Collaborate to solve a shared objective
Compete for resources or rewards
Negotiate, coordinate, or plan as a group
Your task:
Create the environment and logic where AgentX interacts with other agents. Focus on emergent behavior, cooperation strategies, competitive tactics, or negotiation skills learned through RL.
Submission
https://forms.gle/WqgAYkf2vgEeEJKQ8
Event Venue
Woxsen University, Kamkole, Sadasivpet, Hyderabad, Kamkole, Telangana 502345, India, Zahirabad
USD 0.00
