Lets Explore How Pearl is Transforming Theory into Practice:
Introducing Meta’s “Pearl”: The Future of Smart Decision-Making with Production ready agents”
Discover How Pearl Transforms Reinforcement Learning into Real-World Miracles
Lets Learn how to use it
To install Pearl, you can simply clone this repo and pip install
git clone https://github.com/facebookresearch/Pearl.git
cd Pearl
pip install -e .
Let’s Start
To kick off a Pearl agent with a classic reinforcement learning environment, here’s a quick example.
First things first, lets import the necessary libraries
from pearl.pearl_agent import PearlAgent
from pearl.action_representation_modules.one_hot_action_representation_module import (
OneHotActionTensorRepresentationModule,
)
from pearl.policy_learners.sequential_decision_making.deep_q_learning import (
DeepQLearning,
)
from pearl.replay_buffers.sequential_decision_making.fifo_off_policy_replay_buffer import (
FIFOOffPolicyReplayBuffer,
)
from pearl.utils.instantiations.environments.gym_environment import GymEnvironment
env = GymEnvironment("CartPole-v1")
num_actions = env.action_space.n
agent = PearlAgent(
policy_learner=DeepQLearning(
state_dim=env.observation_space.shape[0],
action_space=env.action_space,
hidden_dims=[64, 64],
training_rounds=20,
action_representation_module=OneHotActionTensorRepresentationModule(
max_number_actions=num_actions
),
),
replay_buffer=FIFOOffPolicyReplayBuffer(10_000),
)
observation, action_space = env.reset()
agent.reset(observation, action_space)
done = False
while not done:
action = agent.act(exploit=False)
action_result = env.step(action)
agent.observe(action_result)
agent.learn()
done = action_result.done
As per facebook,
More detailed tutorial will be presented at NeurIPS 2023 EXPO presentation (12/10/2023, 4 pm to 6 pm). Users can replace the environment with any real-world problems.