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Adventures in AI: Duel of the Digital Minds - Exploring the RPG World with Two Unique LLM Architectures, Act 2

Building on the foundation in "Navigating New Realms: Open-Source LLMs in RPG Environments," our follow-up exploration pushes the envelope further by comparing the outcomes of two different models in controlling a player within a virtual RPG world. The first model, a 7B Dolphin2.2-Mistral, demonstrated impressive capabilities in navigating and strategizing within a complex, dynamically rendered RPG landscape. This model, built on interpreting textual descriptions of the environment, showcased the potential of LLMs in spatial reasoning, decision-making, and problem-solving without visual perception.



Our latest venture introduces a new contender: a mixture of expert (MoE) model comprising eight instances of the 7B model (Mistral 8x7b), designed to operate in concert to enhance decision-making and strategic planning. This innovative approach aims to leverage the strengths of multiple models to achieve superior navigation and objective completion within the same RPG setting. The primary objective of this comparison is to observe whether the collaborative intelligence of the MoE model can outperform the single 7B model in terms of efficiency, strategy development, and overall game performance.


Experiment Design


The experiment maintains the original setup: a virtual realm filled with hazards, resources, barriers, and a hidden exit; all described textually to the models. The challenge remains to navigate this world, avoid or overcome obstacles, gather resources, and find the exit. However, this time, we introduce the MoE model as a new variable, running parallel experiments to compare its performance directly against the single 7B model.


Key Comparisons


  1. Decision-Making Efficiency: Evaluating how quickly each model decides on movements and strategies, considering the complexity of the virtual environment.

  2. Strategic Depth: Observing the models' ability to plan and execute long-term strategies, including resource management and hazard avoidance.

  3. Adaptability: Assessing how well each model adapts to new information or changes within the game world, such as suddenly appearing obstacles or depleted resources.

  4. Memory Utilization: Comparing the effectiveness of memory integration in both models, particularly in preventing repetitive or counterproductive behaviors.



A sample run using a single 7B (mistral) Model
With 8x7B Mixture of Expert Model (Mixtral)


Elements of the Game

Key Observations


Diving into the digital realm, we've got two AI heroes: Mistral, the solo 7B adventurer, and Mixtral, the Mixture of Experts (MoE) powerhouse, both showcasing their moves in the grand RPG world. These findings aren't just cool insights into AI parkour; they're a peek into the future of gaming and AI teamwork.


1. Quick on the Draw


Both the Mistral(7b) and Mixtral(8x7b) models demonstrate proficiency in understanding prompts and generating outputs conducive to controlling a player character in a pyGame environment. This effectiveness in translating textual instructions into navigational actions underscores the potential for integrating LLMs as decision-support assistants in complex dynamic environments.


2. The Great Pathfinding Adventure


Now, let's talk style because Mistral and Mixtral are dancing to different tunes:


  • Mistral's Groovy Detours: Mistral's all about that jazz, taking curvy, scenic routes like it's exploring the game world with a metal detector, looking for hidden treasures. This approach screams adventure, ready to adapt and roll with the punches, perfect for games that throw curveballs just for kicks.

  • Mixtral's Beeline Brigade: Then there’s Mixtral, the MoE model, slicing through the game world with straight-line determination, eyes on the prize. It's like it's got a built-in GPS set to the most efficient route, pooling collective genius to hit targets with sniper precision. This strategy's all about the endgame, making every move a step towards victory.

In the showdown of pathfinding, we’ve got Mistral, the improvising explorer, versus Mixtral, the strategic navigator, highlighting the cool contrasts in AI gaming strategies. Whether it's taking the scenic route or cutting straight to the chase, these AI trailblazers are redefining how we play, plan, and plunge into virtual adventures.


3. Global Strategy versus Local Adaptability


The Mixtral model, with its Mixture of Experts (MoE) tech, is like a strategic mastermind in the RPG realm, blending long-term goal-setting with the agility to pivot on a dime. Picture it zipping straight towards its goal, then suddenly, it pulls a 180—realizing it's headed the wrong way. But here’s the twist: this isn’t a setback; it's a clever ruse. Mixtral's not just correcting its course; it’s recalculating, turning a whoops moment into a strategic win by weighing risks and hoarding resources like a survivalist prepping for the apocalypse. It even plays it cool by strolling past the portal, grabbing extra health boosts like a kid in a candy store, before sauntering back to victory.


On the flip side, Mistral is the improviser, taking the scenic route through the game world. It’s all about the journey, weaving through obstacles with the grace of a dancer, making split-second decisions that keep it dancing on the edge.


Together, Mistral and Mixtral showcase the spectrum of AI brilliance in gaming: one’s jazzing it up with nimble moves, while the other’s playing chess, thinking five moves ahead. It’s a dynamic duo of strategic depth and on-the-fly ingenuity, proving that in the virtual world, AI can not only think fast but think forward.



Run with MOE 8x7b model


Conclusion: Architecting AI's Future - How LLMs Shape Decision-Making Strategies


The virtual adventures of Mistral and Mixtral extend beyond gaming excellence, highlighting a pivotal insight: the architecture of Large Language Models (LLM) deeply influences their decision-making strategies. This discovery sheds light on the nuanced relationship between an LLM's structural design and its approach to problem-solving, revealing that the choice of architecture—be it a singular model like Mistral or a Mixture of Experts (MoE) framework like Mixtral—dictates the AI's path through challenges.


This distinction in navigational tactics between Mistral and Mixtral isn't just a feature; it's a window into the customizable nature of AI decision-making. It demonstrates that by aligning LLM architecture with specific goals, we can guide AI towards more effective solutions across diverse domains, from healthcare and urban planning to crisis management.

Thus, the story of Mistral and Mixtral transcends a mere comparison of AI capabilities. It signals a broader, more profound potential: the ability to tailor AI strategies through architectural choices, promising a future where AI's problem-solving prowess is not just a tool but a transformative force, enhancing human decision-making and expanding the boundaries of what we can achieve.

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