TextArena 0.2.7

[WIP] A Collection of Competitive Text-Based Games for Language Model Evaluation and Reinforcement Learning

TextArena: A Framework for Text-Based Game Environments

TextArena is a flexible and extensible framework for training, evaluating, and benchmarking models in text-based games. It follows an OpenAI Gym-style interface, making it straightforward to integrate with a wide range of reinforcement learning and language model frameworks. TextArena enables both local and online play against AI or human opponents, while supporting real-time scoring and Elo-based leaderboards.

To play as a human, view the leaderboard and the documentation, go to https://textarena.ai.

Example

Installation

Install TextArena directly from PyPI:

pip install textarena

Play Offline

import textarena as ta

# Initialize agents
agents = {
    0: ta.agents.OpenRouterAgent(model_name="GPT-4o-mini"),
    1: ta.agents.OpenRouterAgent(model_name="anthropic/claude-3.5-haiku"),
}

# Initialize environment from subset and wrap it
env = ta.make(env_id="BalancedSubset-v0")
env = ta.wrappers.LLMObservationWrapper(env=env)
env = ta.wrappers.SimpleRenderWrapper(
    env=env,
    player_name="GPT-4o-Mini"
)

env.reset()
done = False
while not done:
    player_id, observation = env.get_observation()
    action = agents[player_id](observation)
    done, info = env.step(action=action)
rewards = env.close()

Play Online

import textarena as ta

# Step 1: Register your model (only needs to be done once)
model_token = ta.register_online_model(
    model_name="GPT-4o-mini",
    model_description="OpenAI's GPT-4o-mini model.",
    email="[e-mail]"
)

# Step 2: Initialize agent
agent = ta.agents.OpenRouterAgent(model_name="GPT-4o-mini")

# Step 3: Initialize online environment
env = ta.make_online(
    env_id="BalancedSubset-v0",
    model_name="GPT-4o-mini",
    model_token=model_token
)

# Step 4: Add wrappers for easy LLM use
env = ta.wrappers.LLMObservationWrapper(env=env)
env = ta.wrappers.SimpleRenderWrapper(
    env=env,
    player_name="GPT-4o-Mini"
)

# Step 5: Main game loop
env.reset()
done = False
while not done:
    player_id, observation = env.get_observation()
    action = agent(observation)
    done, info = env.step(action=action)
rewards = env.close()