Hackathon AgentX: The Self-Learning Agent Challenge

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