Poke-Env
1
Poke-Env is an open-source Python framework that provides an interactive Pokémon battle environment, baseline agent implementations, and utilities to develop, train, and evaluate AI agents on Pokémon Showdown. It supports synchronous and asynchronous battle simulations, integrates with popular reinforcement learning libraries, and offers event-driven callbacks for custom policies. Researchers and developers can easily benchmark strategies, monitor performance metrics, and deploy agents for competitive matchups.
AI Platforms & Frameworks
Development Environment
Tool Libraries
#AI Agents
#Pokémon Showdown
#battle simulations
#Python API
#reinforcement learning
#custom strategies
#event-driven architecture
#Performance Analytics
#parallel simulations
#action logging
#algorithm design
#game state parsing
#research reproducibility
#turn-based combat
#AI Development
#baseline agents
#networking abstraction
#performance tuning
#strategy benchmarking
#interactive environment
...
Promote this Tool
Update this Tool
Poke-Env is an open-source Python framework that provides an interactive Pokémon battle environment, baseline agent implementations, and utilities to develop, train, and evaluate AI agents on Pokémon Showdown. It supports synchronous and asynchronous battle simulations, integrates with popular reinforcement learning libraries, and offers event-driven callbacks for custom policies. Researchers and developers can easily benchmark strategies, monitor performance metrics, and deploy agents for competitive matchups.
AI Platforms & Frameworks
Development Environment
Tool Libraries
#AI Agents
#Pokémon Showdown
#battle simulations
#Python API
#reinforcement learning
#custom strategies
#event-driven architecture
#Performance Analytics
#parallel simulations
#action logging
#algorithm design
#game state parsing
#research reproducibility
#turn-based combat
#AI Development
#baseline agents
#networking abstraction
#performance tuning
#strategy benchmarking
#interactive environment
...
Featured
What is Poke-Env?
Poke-Env is designed to streamline the creation and evaluation of AI agents for Pokémon Showdown battles by providing a comprehensive Python interface. It handles communication with the Pokémon Showdown server, parses game state data, and manages turn-by-turn actions through an event-driven architecture. Users can extend base player classes to implement custom strategies using reinforcement learning or heuristic algorithms. The framework offers built-in support for battle simulations, parallelized matchups, and detailed logging of actions, rewards, and outcomes for reproducible research. By abstracting low-level networking and parsing tasks, Poke-Env allows AI researchers and developers to focus on algorithm design, performance tuning, and comparative benchmarking of battle strategies.
Who will use Poke-Env?
- AI researchers
- Reinforcement learning developers
- Game AI enthusiasts
- Educators and students in AI
How to use the Poke-Env?
- Step1: Install poke-env via pip: pip install poke-env
- Step2: Configure Showdown credentials or set up a local server
- Step3: Import Poke-Env classes and define a custom player inheriting from BasePlayer
- Step4: Implement choose_move and event handlers or integrate your RL model
- Step5: Run battles or a tournament loop and collect performance metrics
- Step6: Analyze logs and refine strategies based on results
Platform
- mac
- windows
- linux
Poke-Env's Core Features & Benefits
The Core Features
- Python API for Pokémon Showdown integration
- Interactive battle environment with synchronous and asynchronous simulations
- Prebuilt baseline agent implementations
- Event-driven architecture for custom policy callbacks
- Integration with reinforcement learning libraries
- Battle logging and performance analytics
The Benefits
- Accelerates AI agent development for Pokémon battles
- Standardized benchmarking and reproducibility
- Abstracts networking and parsing complexities
- Easily extensible for custom strategies
- Enables parallelized simulations for faster training
Poke-Env's Main Use Cases & Applications
- Reinforcement learning research on turn-based battles
- Benchmarking AI strategies in Pokémon Showdown
- Educational tutorials on game AI development
- AI competitions and tournaments for Pokémon agents
FAQs of Poke-Env
Poke-Env Company Information
- Poke-Env
Analytic of Poke-Env
Visit Over Time
Monthly Visits
748
Avg Visit Duration
00:10:18
Page Per Visit
4.47
Bounce Rate
40.48%
Sep 2025 - Nov 2025 All Traffic
Geography
Top 2 Regions
United States
62.51%
United Kingdom
37.49%
Sep 2025 - Nov 2025 Worldwide Desktop Only
Traffic Sources
Direct
47.89%
Search
38.49%
Social
6.46%
Referrals
6.17%
Paid Referrals
0.96%
Mail
0.04%
Sep 2025 - Nov 2025 Desktop Only
Poke-Env Reviews
5/5
Poke-Env's Main Competitors and alternatives?
- OpenAI Gym
- PettingZoo
- RLlib
- Gymnasium
- Stable Baselines3
You may also like:
AI design agent automates workflow creating images, videos, 3D models up to 10x faster.
AI-driven chatbot for top OnlyFans creators.
Aixbt is a tokenized AI Agent optimizing revenue across applications.
theGist AI Workspace unifies work apps with AI for improved productivity.
Generate brand visuals and copy using AI to boost e-commerce sales.
GPTConsole is an AI agent designed for streamlined conversation and task automation.
GenSphere is an AI agent that automates data analysis and provides insights for informed decision-making.
Nullify automates the entire AppSec program for security teams using AI-driven solutions.
Refly.AI empowers non-technical creators to automate workflows using natural language and a visual canvas.
Langbase is an AI agent that generates and analyzes natural language content efficiently.
AiTerm: AI Terminal Assistant converting natural language to commands.
Generate intriguing facts effortlessly with our AI-powered tool.
Automate your job applications and find the perfect job with AI technology.
Intellika AI enables seamless automation of data analysis and reporting for businesses.
ScholarRoll helps students find and apply for scholarships easily.
OneReach AI simplifies interactions by automating customer engagement through intelligent messaging.
Phoenix AI Assistant helps streamline tasks using intelligent automation and personalized support.
Flowith is a canvas-based agentic workspace which offers free 🍌Nano Banana Pro and other effective models...
Letta is an AI agent that handles email responses efficiently and accurately.
Windsurf AI Agent helps optimize windsurfing conditions and gear recommendations.
Cody AI helps developers write, review, and understand code efficiently.
A solution for building customizable AI agents with LangChain on AWS Bedrock, leveraging foundation models and custom tools.
scenario-go is a Go SDK for defining complex LLM-driven conversational workflows, managing prompts, context, and multi-step AI tasks.
A ROS-based framework for multi-robot collaboration enabling autonomous task allocation, planning, and coordinated mission execution in teams.
An open-source visual IDE enabling AI engineers to build, test, and deploy agentic workflows 10x faster.
LangGraph Learn offers an interactive GUI to design and execute graph-based AI agent workflows, visualizing language model chains.
AIDE provides AI-powered code generation, debugging, documentation and package management within an integrated web IDE.
A methodology offering twelve best practices to design, configure, and deploy scalable, maintainable AI Agents.
A Python framework for constructing multi-step reasoning pipelines and agent-like workflows with large language models.
SARL is an agent-oriented programming language and runtime providing event-driven behaviors and environment simulation for multi-agent systems.
AI Library is a developer platform for building and deploying customizable AI agents using modular chains and tools.
RModel is an open-source AI agent framework orchestrating LLMs, tool integration, and memory for advanced conversational and task-driven applications.
Provides a FastAPI backend for visual graph-based orchestration and execution of language model workflows in LangGraph GUI.
CodeBeaver is an AI agent that assists in coding and debugging tasks efficiently.
AveHR is an AI-driven human resources agent for streamlining HR tasks.
OpenSpiel provides a library of environments and algorithms for research in reinforcement learning and game theoretic planning.
An autonomous AI agent that writes, tests, and refactors code projects using LLMs with iterative test-driven development.
Clone, Design, and Create Expressive AI Voices in Seconds, with Perfect Sound Effects and Music.
A Python-based framework implementing flocking algorithms for multi-agent simulation, enabling AI agents to coordinate and navigate dynamically.
An open-source framework enabling autonomous LLM agents with retrieval-augmented generation, vector database support, tool integration, and customizable workflows.
An AI agent template showing automated task planning, memory management, and tool execution via OpenAI API.
Pipe Pilot is a Python framework that orchestrates LLM-driven agent pipelines, enabling complex multi-step AI workflows with ease.
Open-source repository providing practical code recipes to build AI agents leveraging Google Gemini's reasoning and tool usage capabilities.
An open-source framework implementing cooperative multi-agent reinforcement learning for autonomous driving coordination in simulation.
Python library with Flet-based interactive chat UI for building LLM agents, featuring tool execution and memory support.
Agentic Workflow is a Python framework to design, orchestrate, and manage multi-agent AI workflows for complex automated tasks.
A GitHub demo showcasing SmolAgents, a lightweight Python framework for orchestrating LLM-powered multi-agent workflows with tool integration.
A Python framework for easily defining and executing AI agent workflows declaratively using YAML-like specifications.
All-in-one AI video creation studio that turns any text and images into full videos up to 30 minutes.
A Pythonic framework implementing the Model Context Protocol to build and run AI agent servers with custom tools.
pyafai is a Python modular framework to build, train, and run autonomous AI agents with plug-in memory and tool support.
LangGraph enables Python developers to construct and orchestrate custom AI agent workflows using modular graph-based pipelines.
A Python wrapper enabling seamless Anthropic Claude API calls through existing OpenAI Python SDK interfaces.
Agent Adapters provides pluggable middleware to integrate LLM-based agents with various external frameworks and tools seamlessly.
Java-Action-Storage is a LightJason module that logs, stores, and retrieves agent actions for distributed multi-agent applications.
LinkAgent orchestrates multiple language models, retrieval systems, and external tools to automate complex AI-driven workflows.