Expert guidance for PyTorch development covering Deep Reinforcement Learning and NLP Transformers.
This skill provides comprehensive knowledge for building RL agents with TorchRL (DQN, PPO) and
NLP systems with HuggingFace Transformers. Use this skill when working with PyTorch 2.7+,
implementing reinforcement learning algorithms, fine-tuning transformer models, or deploying
ML systems to production. Includes current best practices, verified library versions (Dec 2025),
and warnings about deprecated APIs.
Expert guidance for PyTorch development covering Deep Reinforcement Learning and NLP Transformers.
This skill provides comprehensive knowledge for building RL agents with TorchRL (DQN, PPO) and
NLP systems with HuggingFace Transformers. Use this skill when working with PyTorch 2.7+,
implementing reinforcement learning algorithms, fine-tuning transformer models, or deploying
ML systems to production. Includes current best practices, verified library versions (Dec 2025),
and warnings about deprecated APIs.
version
1.0.0
category
machine-learning
triggers
["pytorch","torch","torchrl","reinforcement learning","RL with pytorch","DQN","Deep Q-Network","PPO","Proximal Policy Optimization","policy gradient","huggingface","transformers","BERT","GPT","fine-tuning","torch.compile","quantization","TorchServe","gymnasium","gym environment","NLP with PyTorch","transformer models","PEFT","LoRA"]
For comprehensive coverage, load the appropriate guide:
Topic
Guide
When to Load
Tensors, Autograd, nn.Module
references/pytorch-fundamentals.md
PyTorch basics, device management
TorchRL, DQN, PPO
references/reinforcement-learning.md
RL algorithms, environments
HuggingFace, BERT, Fine-tuning
references/nlp-transformers.md
NLP tasks, transformer models
torch.compile, Quantization, DDP
references/optimization-deployment.md
Production, performance
CLIP, RLHF, Ethics
references/advanced-topics.md
Multi-modal, responsible AI
Common Patterns
Gymnasium Environment (Modern API)
import gymnasium as gym
env = gym.make("CartPole-v1")
obs, info = env.reset()
whileTrue:
action = env.action_space.sample()
obs, reward, terminated, truncated, info = env.step(action) # 5 values!
done = terminated or truncated
if done:
break
Training Loop with AMP
from torch.cuda.amp import autocast, GradScaler
scaler = GradScaler()
for batch in dataloader:
optimizer.zero_grad()
with autocast():
loss = compute_loss(batch)
scaler.scale(loss).backward()
scaler.step(optimizer)
scaler.update()
PEFT/LoRA Fine-tuning
from peft import LoraConfig, get_peft_model
lora_config = LoraConfig(r=8, lora_alpha=16, target_modules=["q_proj", "v_proj"])
model = get_peft_model(model, lora_config)
# Now fine-tune with much fewer parameters
