Build an AI-Powered Valorant E-sports Manager with AWS Bedrock

AI is revolutionizing entertainment and esports, and this is especially true in the highly competitive world of esports. Gamers can greatly benefit from an AI assistant or manager to build the ultimate team. Such a tool could leverage vast datasets to identify patterns and strategies undetectable by the human eye. Let's explore building an AI-powered esports manager—specifically, a Valorant Team Builder—to help you construct your dream team and dominate the competition.

Learning Outcomes

• Grasp the significance of team composition in Valorant for maximizing performance and strategic advantage.
• Learn to utilize AI-driven insights for crafting balanced and highly effective teams.
• Explore customization options to fine-tune team roles and strategies based on individual player strengths.
• Develop skills in performance tracking to assess and improve team dynamics over time.
• Learn best practices for sharing and saving team configurations for future matches.

*This article is part of the***Data Science Blogathon.

Table of contents

• Learning Outcomes
• Developing an AI Manager with AWS Bedrock
• Essential Steps for Data Preparation
• Developing the User Interface
• Building the backend: Generative AI with AWS Bedrock
• Notes on AWS Bedrock
• Conclusion
• Frequently Asked Questions

Developing an AI Manager with AWS Bedrock

This AI manager, built using AWS Bedrock, is specifically designed for managing and enhancing Valorant gameplay. It employs advanced machine learning models to analyze player performance, offer strategic advice, and optimize team compositions. By integrating AWS Bedrock's capabilities, we aim to create a tool that not only helps players improve their skills but also boosts their overall enjoyment of the game. Our approach focuses on data collection, analysis, and actionable insights to help players reach the top tier of Valorant competition.

Essential Steps for Data Preparation

We'll generate synthetic data, loosely mirroring real-world player data found in a Kaggle dataset. A Python script generates artificial values for each in-game metric based on the player's character. Key metrics include:

• ACS (Average Combat Score): A measure of a player's overall impact, considering damage, kills, and round contributions.
• KDA Ratio: (Kills Assists) / Deaths, indicating survivability and team contribution.
• Headshot Percentage: The percentage of headshots, reflecting aim and precision.
• ADR (Average Damage per Round): The average damage dealt per round, showing damage consistency.

This data is used to create a SQLite database using a Python script (sqlite.pyscript). An example of data generation for a "Duelist" role is shown below:

if role == "Duelist":
  average_combat_score = round(np.random.normal(300, 30), 1)
  kill_deaths = round(np.random.normal(1.5, 0.3), 2)
  average_damage_per_round = round(np.random.normal(180, 20), 1)
  kills_per_round = round(np.random.normal(1.5, 0.3), 2)
  assists_per_round = round(np.random.normal(0.3, 0.1), 2)
  first_kills_per_round = round(np.random.uniform(0.1, 0.4), 2)
  first_deaths_per_round = round(np.random.uniform(0.0, 0.2), 2)
  headshot_percentage = round(np.random.uniform(25, 55), 1)
  clutch_success_percentage = round(np.random.uniform(15, 65), 1)

Based on user requests (e.g., "Build a professional team"), the system queries the database for optimal players. Functions like get_agents_by_role, get_organizations, and get_regions provide contextual data. The sample synthetic data is available here. Integration with real-world data via the Riot API is also possible.

Developing the User Interface

The frontend, built with Streamlit, allows users to specify team type and constraints. These inputs determine the SQL query used on the SQLite database.

An example of query selection based on team type:

try:
    if team_type == "Professional Team Submission":
        query = """
        SELECT * FROM players
        WHERE org IN ('Ascend', 'Mystic', 'Legion', 'Phantom', 'Rising', 'Nebula', 'OrgZ', 'T1A')
        """
    # ... other team types ...

The selected players are then used to construct a prompt for the LLM, requesting an analysis of their strengths and weaknesses. The LLM's response provides an analysis and suggests adjustments.

Building the Interface Using a Wrapper

The frontend interacts with AWS via the boto3 library using a wrapper for the invoke_agent() method. This simplifies interaction with the AWS SDK.

class BedrockAgentRuntimeWrapper:
    # ... (wrapper code as before) ...

An instance of the wrapper is initialized, and requests are sent to the AI agent using a boto3 client containing agent details (Agent ID, Agent Alias ID, session ID, and prompt).

try:
    runtime_client = boto3.client("bedrock-agent-runtime", ...)
    bedrock_wrapper = BedrockAgentRuntimeWrapper(runtime_client)
    output_text = bedrock_wrapper.invoke_agent(agent_id, agent_alias_id, session_id, prompt)
    print(f"Agent Response: {output_text}")

The LLM's flexibility can be adjusted to balance accuracy and creativity through temperature settings or prompt engineering.

Building the backend: Generative AI with AWS Bedrock

Access the AWS Bedrock console, request model access (Claude and Titan embeddings are recommended), and create an S3 bucket to store your documents (data on player types, strategies, and metric interpretation guidelines). Create a Knowledge Base (KB) linking to this S3 bucket. The KB uses OpenSearch Serverless (OSS) to create a vector database. Create an AI agent, specifying the KB and adding instructions like:

<code>"You are an expert at Valorant player and analyst. Answer the questions given
to you using the knowledge base as a reference only."</code>

The application uses several environment variables (see table in original response).

Some notes on AWS Bedrock

Remember these key points when working with AWS Bedrock:

• Root accounts cannot create AI agents; use an IAM user.
• Configure appropriate IAM policies for S3, KB, Agents, OSS, and Lambda access.
• Delete resources after use to avoid costs.
• Contact AWS support for billing inquiries.

Conclusion

This article details building a RAG toolchain with AWS Bedrock. AI tools are transforming various fields, and this example demonstrates their potential in esports.

Key Takeaways

• Build an AI-powered Valorant Team Builder for optimized team creation.
• Leverage data-driven insights for strategic team composition.
• Use a user-friendly app to select top-tier players based on performance data.

Frequently Asked Questions

(Same as original response)

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