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refactor GymnasiumAgent (#3927)

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refactor GymnasiumAgent (for single-agent environments) to be extensible
to PettingZooAgent (multi-agent environments)
fix_agent_callbacks
mbchang 1 year ago committed by GitHub
parent 81601d886c
commit ffc87233a1
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1 changed files with 61 additions and 45 deletions
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106
docs/use_cases/agent_simulations/gymnasium.ipynb
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@ -12,22 +12,38 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 1,
"id": "f36427cf",
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: gymnasium in /Users/michaelchang/.miniconda3/envs/langchain/lib/python3.9/site-packages (0.28.1)\n",
"Requirement already satisfied: farama-notifications>=0.0.1 in /Users/michaelchang/.miniconda3/envs/langchain/lib/python3.9/site-packages (from gymnasium) (0.0.4)\n",
"Requirement already satisfied: importlib-metadata>=4.8.0 in /Users/michaelchang/.miniconda3/envs/langchain/lib/python3.9/site-packages (from gymnasium) (6.0.1)\n",
"Requirement already satisfied: cloudpickle>=1.2.0 in /Users/michaelchang/.miniconda3/envs/langchain/lib/python3.9/site-packages (from gymnasium) (2.2.1)\n",
"Requirement already satisfied: numpy>=1.21.0 in /Users/michaelchang/.miniconda3/envs/langchain/lib/python3.9/site-packages (from gymnasium) (1.24.3)\n",
"Requirement already satisfied: jax-jumpy>=1.0.0 in /Users/michaelchang/.miniconda3/envs/langchain/lib/python3.9/site-packages (from gymnasium) (1.0.0)\n",
"Requirement already satisfied: typing-extensions>=4.3.0 in /Users/michaelchang/.miniconda3/envs/langchain/lib/python3.9/site-packages (from gymnasium) (4.5.0)\n",
"Requirement already satisfied: zipp>=0.5 in /Users/michaelchang/.miniconda3/envs/langchain/lib/python3.9/site-packages (from importlib-metadata>=4.8.0->gymnasium) (3.15.0)\n"
]
}
],
"source": [
"!pip install gymnasium"
]
},
{
"cell_type": "code",
"execution_count": 50,
"execution_count": 2,
"id": "f9bd38b4",
"metadata": {},
"outputs": [],
"source": [
"import gymnasium as gym\n",
"import inspect\n",
"import tenacity\n",
"\n",
"from langchain.chat_models import ChatOpenAI\n",
@ -50,20 +66,20 @@
},
{
"cell_type": "code",
"execution_count": 51,
"execution_count": 3,
"id": "870c24bc",
"metadata": {},
"outputs": [],
"source": [
"def get_docs(env):\n",
" while 'env' in dir(env):\n",
" env = env.env\n",
" return env.__doc__\n",
"\n",
"class Agent():\n",
"class GymnasiumAgent():\n",
" @classmethod\n",
" def get_docs(cls, env):\n",
" return env.unwrapped.__doc__\n",
" \n",
" def __init__(self, model, env):\n",
" self.model = model\n",
" self.docs = get_docs(env)\n",
" self.env = env\n",
" self.docs = self.get_docs(env)\n",
" \n",
" self.instructions = \"\"\"\n",
"Your goal is to maximize your return, i.e. the sum of the rewards you receive.\n",
@ -90,22 +106,17 @@
" self.message_history = []\n",
" self.ret = 0\n",
" \n",
" def reset(self, obs):\n",
" def random_action(self):\n",
" action = self.env.action_space.sample()\n",
" return action\n",
" \n",
" def reset(self):\n",
" self.message_history = [\n",
" SystemMessage(content=self.docs),\n",
" SystemMessage(content=self.instructions),\n",
" ]\n",
" obs_message = f\"\"\"\n",
"Observation: {obs}\n",
"Reward: 0\n",
"Termination: False\n",
"Truncation: False\n",
"Return: 0\n",
" \"\"\"\n",
" self.message_history.append(HumanMessage(content=obs_message))\n",
" return obs_message\n",
" \n",
" def observe(self, obs, rew, term, trunc, info):\n",
" def observe(self, obs, rew=0, term=False, trunc=False, info=None):\n",
" self.ret += rew\n",
" \n",
" obs_message = f\"\"\"\n",
@ -117,16 +128,25 @@
" \"\"\"\n",
" self.message_history.append(HumanMessage(content=obs_message))\n",
" return obs_message\n",
" \n",
" @tenacity.retry(stop=tenacity.stop_after_attempt(2),\n",
" wait=tenacity.wait_none(), # No waiting time between retries\n",
" retry=tenacity.retry_if_exception_type(ValueError),\n",
" before_sleep=lambda retry_state: print(f\"ValueError occurred: {retry_state.outcome.exception()}, retrying...\"),\n",
" retry_error_callback=lambda retry_state: 0) # Default value when all retries are exhausted\n",
" def act(self):\n",
" \n",
" def _act(self):\n",
" act_message = self.model(self.message_history)\n",
" self.message_history.append(act_message)\n",
" action = int(self.action_parser.parse(act_message.content)['action'])\n",
" return action\n",
" \n",
" def act(self):\n",
" try:\n",
" for attempt in tenacity.Retrying(\n",
" stop=tenacity.stop_after_attempt(2),\n",
" wait=tenacity.wait_none(), # No waiting time between retries\n",
" retry=tenacity.retry_if_exception_type(ValueError),\n",
" before_sleep=lambda retry_state: print(f\"ValueError occurred: {retry_state.outcome.exception()}, retrying...\"),\n",
" ):\n",
" with attempt:\n",
" action = self._act()\n",
" except tenacity.RetryError as e:\n",
" action = self.random_action()\n",
" return action"
]
},
@ -140,13 +160,13 @@
},
{
"cell_type": "code",
"execution_count": 52,
"execution_count": 4,
"id": "9e902cfd",
"metadata": {},
"outputs": [],
"source": [
"env = gym.make(\"Blackjack-v1\")\n",
"agent = Agent(model=ChatOpenAI(temperature=0.2), env=env)"
"agent = GymnasiumAgent(model=ChatOpenAI(temperature=0.2), env=env)"
]
},
{
@ -159,7 +179,7 @@
},
{
"cell_type": "code",
"execution_count": 53,
"execution_count": 5,
"id": "ad361210",
"metadata": {},
"outputs": [
@ -168,7 +188,7 @@
"output_type": "stream",
"text": [
"\n",
"Observation: (10, 3, 0)\n",
"Observation: (15, 4, 0)\n",
"Reward: 0\n",
"Termination: False\n",
"Truncation: False\n",
@ -176,19 +196,11 @@
" \n",
"Action: 1\n",
"\n",
"Observation: (18, 3, 0)\n",
"Reward: 0.0\n",
"Termination: False\n",
"Truncation: False\n",
"Return: 0.0\n",
" \n",
"Action: 0\n",
"\n",
"Observation: (18, 3, 0)\n",
"Reward: 1.0\n",
"Observation: (25, 4, 0)\n",
"Reward: -1.0\n",
"Termination: True\n",
"Truncation: False\n",
"Return: 1.0\n",
"Return: -1.0\n",
" \n",
"break True False\n"
]
@ -196,14 +208,18 @@
],
"source": [
"observation, info = env.reset()\n",
"obs_message = agent.reset(observation)\n",
"agent.reset()\n",
"\n",
"obs_message = agent.observe(observation)\n",
"print(obs_message)\n",
"\n",
"while True:\n",
" action = agent.act()\n",
" observation, reward, termination, truncation, info = env.step(action)\n",
" obs_message = agent.observe(observation, reward, termination, truncation, info)\n",
" print(f'Action: {action}')\n",
" print(obs_message)\n",
" \n",
" if termination or truncation:\n",
" print('break', termination, truncation)\n",
" break\n",

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