  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.
