torchrl.modules package¶
TorchRL offers a comprehensive collection of RL-specific neural network modules built on top of
tensordict.nn.TensorDictModule. These modules are designed to work seamlessly with
tensordict data structures, making it easy to build and compose RL models.
Key Features¶
Spec-based construction: Automatically configure output layers based on action specs
Probabilistic modules: Built-in support for stochastic policies
Exploration strategies: Modular exploration wrappers (ε-greedy, Ornstein-Uhlenbeck, etc.)
Value networks: Q-value, distributional, and dueling architectures
Safe modules: Automatic projection to satisfy action constraints
Model-based RL: World model and dynamics modules
Quick Example¶
from torchrl.modules import ProbabilisticActor, TanhNormal
from torchrl.envs import GymEnv
from tensordict.nn import TensorDictModule
import torch.nn as nn
env = GymEnv("Pendulum-v1")
# Create a probabilistic actor
actor = ProbabilisticActor(
module=TensorDictModule(
nn.Sequential(nn.Linear(3, 64), nn.ReLU(), nn.Linear(64, 2)),
in_keys=["observation"],
out_keys=["loc", "scale"],
),
in_keys=["loc", "scale"],
distribution_class=TanhNormal,
spec=env.action_spec,
)
