Agent framework for constructing language model agents and training on constructive tasks.

This repo models agent-environment interactions using a Partially Observable Markov Decision Process (POMDP). Inspired by POMDP, this repo's name ldp stands for Language Decision Processes.

To install ldp:

pip install -e .

If you plan to export Graphviz visualizations, make sure you also install the graphviz library into your OS via:

• Linux: apt install graphviz
• macOS: brew install graphviz

An agent should have two functions:

agent_state = await agent.init_state(tools=tools)
new_action, new_agent_state, value = await agent.get_asv(
    agent_state, obs
)

An agent should have a function get_asv(agent_state, obs) that chooses an action (a) from the observation messages, and returns the next agent state (s) and a value estimate (v). The first argument, agent_state, is a state specific for the agent that can be used for training from episodes. You can make it None if you aren't using it. It could contain things like agent memory.

The obs are not the complete list of observations, but rather the last list from env.step. The agent should keep track of observations via its state if it would like to keep them.

The value can be 0, it is the agent's estimate of the future rewards given its state and observations. This is used for training.

The Agent (as well as classes in agent.ops) are generics, which means:

• Agent is designed to support arbitrary types
• Subclasses can exactly specify state types, making the code more readable

If you are new to Python generics (typing.Generic), please read about them in Python typing.

Below is how to specify an agent with a custom state type.

from dataclasses import dataclass, field
from datetime import datetime

from ldp.agents import Agent


@dataclass
class MyComplexState:
    vector: list[float]
    timestamp: datetime = field(default_factory=datetime.now)


class MyAgent(Agent[MyComplexState]):
    """Some agent who is now type checked to match the custom state."""

from ldp.agent import SimpleAgent
from aviary.env import DummyEnv

env = DummyEnv()
agent = SimpleAgent()

obs, tools = await env.reset()
agent_state = await agent.init_state(tools=tools)

done = False
while not done:
    action, agent_state, _ = await agent.get_asv(agent_state, obs)
    obs, reward, done, truncated = await env.step(action.value)