REVIEW 5 cited by
PokeLLMon: A Human-Parity Agent for Pokemon Battles with Large Language Models
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
We introduce PokeLLMon, the first LLM-embodied agent that achieves human-parity performance in tactical battle games, as demonstrated in Pokemon battles. The design of PokeLLMon incorporates three key strategies: (i) In-context reinforcement learning that instantly consumes text-based feedback derived from battles to iteratively refine the policy; (ii) Knowledge-augmented generation that retrieves external knowledge to counteract hallucination and enables the agent to act timely and properly; (iii) Consistent action generation to mitigate the panic switching phenomenon when the agent faces a powerful opponent and wants to elude the battle. We show that online battles against human demonstrates PokeLLMon's human-like battle strategies and just-in-time decision making, achieving 49% of win rate in the Ladder competitions and 56% of win rate in the invited battles. Our implementation and playable battle logs are available at: https://github.com/git-disl/PokeLLMon.
Forward citations
Cited by 5 Pith papers
-
Society of Mind Meets Real-Time Strategy: A Hierarchical Multi-Agent Framework for Strategic Reasoning
A hierarchical framework of specialized imitation agents plus a strategic planner improves win rates and cuts LLM calls in text-based StarCraft II across all race matchups.
-
InstantEdit: Text-Guided Few-Step Image Editing with Piecewise Rectified Flow
InstantEdit combines RectifiedFlow inversion, latent injection, disentangled prompt guidance, and Canny ControlNet to do fast few-step text-guided image editing with content preservation.
-
Automated Skill Discovery for Language Agents through Exploration and Iterative Feedback
EXIF repeatedly has a teacher agent explore an environment, relabel the exploration as tasks, train a student agent on it, and use the student's failures to guide the next round, improving 7B-8B agents in Webshop and Crafter.
-
Game Theory Meets Large Language Models: A Systematic Survey with Taxonomy and New Frontiers
A taxonomy-based survey of bidirectional game theory and LLM research, spanning evaluation, alignment, economic competition, and LLM-driven game solving.
-
Think in Games: Learning to Reason in Games via Reinforcement Learning with Large Language Models
A reinforcement-learning pipeline for predicting macro-actions in Honor of Kings improves action prediction accuracy, but the method is imitation of human replay labels, not the claimed environmental interaction.
Discussion (0). Continue with ORCID to comment.