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new example: single agent, simulated environment (openai gym) (#3758)
For many applications of LLM agents, the environment is real (internet, database, REPL, etc). However, we can also define agents to interact in simulated environments like text-based games. This is an example of how to create a simple agent-environment interaction loop with [Gymnasium](https://github.com/Farama-Foundation/Gymnasium) (formerly [OpenAI Gym](https://github.com/openai/gym)).
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mbchang
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@ -8,6 +8,9 @@ Agent simulations generally involve two main components:
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Specific implementations of agent simulations (or parts of agent simulations) include
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## Simulations with One Agent
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- [Simulated Environment: Gymnasium](agent_simulations/agent_with_simulated_environment.ipynb): an example of how to create a simple agent-environment interaction loop with [Gymnasium](https://github.com/Farama-Foundation/Gymnasium) (formerly [OpenAI Gym](https://github.com/openai/gym)).
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## Simulations with Two Agents
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- [CAMEL](agent_simulations/camel_role_playing.ipynb): an implementation of the CAMEL (Communicative Agents for “Mind” Exploration of Large Scale Language Model Society) paper, where two agents communicate with each other.
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- [Two Player D&D](agent_simulations/two_player_dnd.ipynb): an example of how to use a generic simulator for two agents to implement a variant of the popular Dungeons & Dragons role playing game.
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233
docs/use_cases/agent_simulations/gymnasium.ipynb
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233
docs/use_cases/agent_simulations/gymnasium.ipynb
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@ -0,0 +1,233 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "4b089493",
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"metadata": {},
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"source": [
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"# Simulated Environment: Gymnasium\n",
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"\n",
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"For many applications of LLM agents, the environment is real (internet, database, REPL, etc). However, we can also define agents to interact in simulated environments like text-based games. This is an example of how to create a simple agent-environment interaction loop with [Gymnasium](https://github.com/Farama-Foundation/Gymnasium) (formerly [OpenAI Gym](https://github.com/openai/gym))."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 50,
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"id": "f9bd38b4",
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"metadata": {},
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"outputs": [],
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"source": [
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"import gymnasium as gym\n",
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"import tenacity\n",
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"\n",
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"from langchain.chat_models import ChatOpenAI\n",
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"from langchain.schema import (\n",
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" AIMessage,\n",
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" HumanMessage,\n",
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" SystemMessage,\n",
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" BaseMessage,\n",
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")\n",
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"from langchain.output_parsers import RegexParser"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e222e811",
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"metadata": {},
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"source": [
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"## Define the agent"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 51,
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"id": "870c24bc",
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_docs(env):\n",
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" while 'env' in dir(env):\n",
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" env = env.env\n",
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" return env.__doc__\n",
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"\n",
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"class Agent():\n",
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" def __init__(self, model, env):\n",
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" self.model = model\n",
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" self.docs = get_docs(env)\n",
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" \n",
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" self.instructions = \"\"\"\n",
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"Your goal is to maximize your return, i.e. the sum of the rewards you receive.\n",
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"I will give you an observation, reward, terminiation flag, truncation flag, and the return so far, formatted as:\n",
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"\n",
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"Observation: <observation>\n",
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"Reward: <reward>\n",
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"Termination: <termination>\n",
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"Truncation: <truncation>\n",
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"Return: <sum_of_rewards>\n",
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"\n",
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"You will respond with an action, formatted as:\n",
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"\n",
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"Action: <action>\n",
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"\n",
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"where you replace <action> with your actual action.\n",
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"Do nothing else but return the action.\n",
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"\"\"\"\n",
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" self.action_parser = RegexParser(\n",
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" regex=r\"Action: (.*)\", \n",
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" output_keys=['action'], \n",
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" default_output_key='action')\n",
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" \n",
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" self.message_history = []\n",
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" self.ret = 0\n",
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" \n",
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" def reset(self, obs):\n",
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" self.message_history = [\n",
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" SystemMessage(content=self.docs),\n",
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" SystemMessage(content=self.instructions),\n",
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" ]\n",
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" obs_message = f\"\"\"\n",
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"Observation: {obs}\n",
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"Reward: 0\n",
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"Termination: False\n",
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"Truncation: False\n",
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"Return: 0\n",
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" \"\"\"\n",
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" self.message_history.append(HumanMessage(content=obs_message))\n",
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" return obs_message\n",
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" \n",
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" def observe(self, obs, rew, term, trunc, info):\n",
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" self.ret += rew\n",
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" \n",
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" obs_message = f\"\"\"\n",
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"Observation: {obs}\n",
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"Reward: {rew}\n",
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"Termination: {term}\n",
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"Truncation: {trunc}\n",
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"Return: {self.ret}\n",
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" \"\"\"\n",
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" self.message_history.append(HumanMessage(content=obs_message))\n",
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" return obs_message\n",
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" \n",
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" @tenacity.retry(stop=tenacity.stop_after_attempt(2),\n",
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" wait=tenacity.wait_none(), # No waiting time between retries\n",
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" retry=tenacity.retry_if_exception_type(ValueError),\n",
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" before_sleep=lambda retry_state: print(f\"ValueError occurred: {retry_state.outcome.exception()}, retrying...\"),\n",
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" retry_error_callback=lambda retry_state: 0) # Default value when all retries are exhausted\n",
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" def act(self):\n",
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" act_message = self.model(self.message_history)\n",
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" self.message_history.append(act_message)\n",
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" action = int(self.action_parser.parse(act_message.content)['action'])\n",
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" return action"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2e76d22c",
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"metadata": {},
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"source": [
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"## Initialize the simulated environment and agent"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 52,
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"id": "9e902cfd",
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"metadata": {},
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"outputs": [],
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"source": [
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"env = gym.make(\"Blackjack-v1\")\n",
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"agent = Agent(model=ChatOpenAI(temperature=0.2), env=env)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e2c12b15",
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"metadata": {},
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"source": [
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"## Main loop"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 53,
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"id": "ad361210",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"Observation: (10, 3, 0)\n",
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"Reward: 0\n",
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"Termination: False\n",
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"Truncation: False\n",
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"Return: 0\n",
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" \n",
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"Action: 1\n",
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"\n",
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"Observation: (18, 3, 0)\n",
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"Reward: 0.0\n",
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"Termination: False\n",
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"Truncation: False\n",
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"Return: 0.0\n",
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" \n",
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"Action: 0\n",
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"\n",
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"Observation: (18, 3, 0)\n",
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"Reward: 1.0\n",
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"Termination: True\n",
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"Truncation: False\n",
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"Return: 1.0\n",
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" \n",
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"break True False\n"
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]
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}
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],
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"source": [
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"observation, info = env.reset()\n",
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"obs_message = agent.reset(observation)\n",
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"print(obs_message)\n",
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"while True:\n",
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" action = agent.act()\n",
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" observation, reward, termination, truncation, info = env.step(action)\n",
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" obs_message = agent.observe(observation, reward, termination, truncation, info)\n",
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" print(f'Action: {action}')\n",
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" print(obs_message)\n",
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" if termination or truncation:\n",
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" print('break', termination, truncation)\n",
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" break\n",
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"env.close()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "58a13e9c",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.16"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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