🤖🦿 Understanding PPO: From Language Generation to Robot Control — Code, Concepts, and Comparisons
Proximal Policy Optimization (PPO) in both large language models (LLMs, e.g., GPT-style) and classical control environments (e.g., Walker2D), focusing on the structure of the PPO update and how actions are selected during inference.
1. 🧾 PPO step() Call — Argument-by-Argument Breakdown
#
ppo_trainer.step(
queries=[input_ids[0]], # Prompt (tokenized) — represents the current state
responses=[response_ids[0]], # Generated tokens — represents the action taken
rewards=[reward] # Scalar from reward model — score for that action
)
Mapping to Classic RL (Walker2D) #
| PPO Argument | 🤖 LLM (RLHF) | 🦿 Walker2D (Classic RL) |
|---|---|---|
queries = [input_ids[0]] |
Prompt as input (discrete tokenized state) | Robot’s continuous state (joint angles, velocities) |
responses = [response_ids[0]] |
Generated tokens (sequence of actions) | Applied joint torques (vector of real numbers) |
rewards = [reward] |
Reward model output (alignment score) | Environment reward (e.g., distance walked) |
2. 🎯 Action Selection in PPO #
How does the agent choose its next action, given a state/prompt?
🤖 LLMs (Text Generation) #
# Given a prompt (state)
input_ids = tokenizer("What causes rain?", return_tensors="pt").input_ids
# Model outputs token logits for next action
outputs = model(input_ids=input_ids)
logits = outputs.logits[:, -1, :]
probs = torch.softmax(logits, dim=-1)
# Sample the next token (action) from distribution
next_token = torch.multinomial(probs, num_samples=1)
# Repeat to generate full response
🦿 Walker2D (Physical Control) #
# Get current robot state
state = get_env_state() # vector like [θ1, θ2, v1, v2...]
# Policy network outputs action distribution parameters
mean, std = policy_net(state)
# Sample a continuous action (e.g., torque values)
action = torch.normal(mean, std)
# Apply action to environment
next_state, reward, done, info = env.step(action)
🔁 Comparison of Action Logic #
| Component | 🤖 LLM (RLHF) | 🦿 Walker2D (Classic RL) |
|---|---|---|
| State | Prompt text | Robot’s physical state |
| Action | Next token (discrete) | Joint torques (continuous) |
| Policy Output | Token logits (softmaxed) | Mean & std dev of Gaussian per action dim |
| Sampling Method | Multinomial over vocab | Sample from Gaussian |
| Result | Extend response with chosen token | Step to new physical state |
🔁 PPO Mapping in LLMs (RLHF) vs Classical RL #
| Category | PPO in LLMs (RLHF) | 🦿 PPO in Walker2D (Classic RL) |
|---|---|---|
| Agent | Language Model (e.g., GPT-2, o1) | Control Policy Network |
| Environment | Static or semi-static prompt context | Physics-based simulator (e.g., MuJoCo) |
| State | Prompt (or full token context so far) | Robot’s current physical state (joint angles, velocity, etc.) |
| Action | Next token in the sequence (discrete, vocabulary-sized) | Torque values for each joint (continuous, multi-dimensional) |
| Trajectory | Sequence of tokens (prompt + response) | Sequence of joint states and actions over time |
| Reward Signal | Given after full response (from reward model trained via human preferences) | Immediate reward at each time step (distance walked, balance maintained, etc.) |
| Reward Nature | Sparse, episodic, scalar (usually one reward per episode) | Dense, frequent, multi-dimensional (continuous feedback per step) |
| Goal | Generate text aligned with human values/preferences | Learn movement to walk forward efficiently without falling |
| Policy Network | Transformer LM (large, ~billions of params) | Feedforward or RNN-based controller (small, e.g., MLP) |
| Reference Model | Frozen copy of base LM (used for KL-penalty regularization) | Usually none (KL not common in Walker2D PPO) |
| Training Stability | Needs KL penalty to prevent mode collapse / nonsense generations | PPO alone is usually enough due to continuous feedback |
| Evaluation | Human evals, reward model scores (e.g., helpfulness, safety) | Distance walked, steps survived, control energy used |
| 🗣️ “Say the right thing, the way a human likes” | 🦿 “Move the right way, so you don’t fall” | |
| Actions are words, optimizing a sequence to match human preference | Actions are forces, optimizing to physically walk and stay balanced | |
| Reference | https://rlhfbook.com/ | https://gymnasium.farama.org/ |