Reinforcement Learning (RL) can feel like a jungle when you first step in — lots of algorithms, fancy names, and subtle differences that matter a lot in practice. Two families you’ll hear about constantly are policy gradient methods and actor-critic methods. This article — Policy Gradient vs Actor-Critic — breaks down what each approach actually means, how they differ, when to pick one over the other, and practical examples to help you decide. I’ll keep the explanations clear, practical, and jargon-light so you can use them right away.
Let’s start with a short, friendly map:
Policy gradient methods directly optimize the policy — the function that maps states to actions.
Actor-critic methods split the work: an actor suggests actions and a critic evaluates how good those actions are.
Both families are powerful. The main difference is that actor-critic methods introduce a learned value function (the critic) to stabilize and guide policy updates. In many real-world tasks, that extra stability is the difference between “it learns slowly” and “it never learns at all.”
What is Policy Gradient Method?
Definition of Policy Gradient — Policy Gradient vs Actor-Critic
A policy gradient method parameterizes the policy
(usually with a neural network) and directly adjusts the parameters
to maximize expected reward. Instead of learning state values or action values, it uses sampled returns from episodes to nudge the policy in directions that produced high returns.
How Policy Gradient Works — Policy Gradient vs Actor-Critic
Run the policy to collect trajectories (state, action, reward).
Compute the return (sum of discounted future rewards) for each action or timestep.
Use the gradient of the log-probability of chosen actions multiplied by the return to update parameters:
Repeat.
This is elegant and simple — you’re nudging the policy to make high-return actions more probable.
Advantages of Policy Gradient — Policy Gradient vs Actor-Critic
Natural fit for continuous action spaces (because policies can output continuous distributions).
Learns stochastic policies inherently — useful when exploration is important.
Conceptually simple and easy to implement (REINFORCE is the classic example).
Limitations of Policy Gradient — Policy Gradient vs Actor-Critic
High variance: returns can be noisy, making learning unstable.
Sample inefficient: needs many episodes to converge.
Sensitive to hyperparameters (learning rate, reward scaling, etc.).
What is Actor-Critic Method?
Definition of Actor-Critic — Policy Gradient vs Actor-Critic
An actor-critic method combines a policy network (actor) with a value network (critic). The critic estimates how good a state (or state-action pair) is, and the actor uses that feedback to update the policy. In short: the critic reduces variance and provides a smoother learning signal.
How Actor-Critic Works — Policy Gradient vs Actor-Critic
Actor proposes actions according to .
Critic estimates a value or .
The policy is updated using an advantage estimate: or a TD-error.
Critic is trained to minimize value prediction error (e.g., temporal-difference loss).
Types include:
A2C/A3C (Advantage Actor-Critic / Asynchronous A2C)
PPO (uses actor-critic with trust-region-style updates)
SAC (soft actor-critic for stochastic policies in continuous spaces)
Advantages of Actor-Critic — Policy Gradient vs Actor-Critic
Lower variance: critic provides a baseline or advantage estimate.
More sample-efficient: bootstrapping (TD learning) often needs fewer samples.
Better suited to longer, complex tasks where pure Monte Carlo returns are noisy.
Limitations of Actor-Critic — Policy Gradient vs Actor-Critic
More moving parts: two networks (or heads) to tune and stabilize.
Potential bias introduced by bootstrapped value estimates.
Implementation can be trickier than vanilla policy gradient.
Policy Gradient vs Actor-Critic: Key Differences
Below are the practical differences summarized. This helps when you’re deciding which approach to use.
Comparison Table – Policy Gradient vs Actor-Critic
| Feature | Policy Gradient (e.g., REINFORCE) | Actor-Critic (e.g., A2C, PPO, SAC) |
|---|---|---|
| Core Idea | Directly optimize policy using sampled returns | Actor proposes actions; Critic evaluates (value function) |
| Update Signal | Monte Carlo returns (full episode) | Bootstrapped TD error / advantage |
| Variance | High | Lower (due to critic baseline) |
| Bias | Low (unbiased Monte Carlo) | Can be biased (bootstrapping) |
| Sample Efficiency | Generally low | Better (uses TD updates) |
| Complexity | Simple | More complex (two models/heads) |
| Stability | Less stable | More stable in practice |
| Use Cases | Simple tasks, testing ideas, continuous actions | Complex tasks, robotics, games (Atari, Mujoco) |
| Popular Algorithms | REINFORCE, VPG | A2C, A3C, PPO, SAC, DDPG, TD3 |
Policy Gradient vs Actor-Critic: Which One Should You Choose?
Short answer: it depends. Below is a practical guide to choosing.
When to choose Policy Gradient
You’re learning and want a simple baseline implementation (REINFORCE).
Your problem has strictly continuous action spaces, and you want a stochastic policy.
You want an unbiased, straightforward Monte Carlo approach to understand theory.
When to choose Actor-Critic
Your environment is complex or high-dimensional (Atari, robotics).
You need faster learning and better sample efficiency.
You want more stable training and can manage extra complexity (tuning critic, handling bias).
Practical tips
Start with a simple policy gradient to understand the mechanics — get a working baseline.
If training is unstable or slow, move to actor-critic (A2C/PPO/SAC depending on action space).
Use advantage normalization, reward scaling, and smaller learning rates to stabilize training.
Test on small environments (CartPole, MountainCar) first before scaling up.
Real-World Examples of Policy Gradient vs Actor-Critic
Example of Policy Gradient in Action — Policy Gradient vs Actor-Critic
REINFORCE on CartPole is a classic beginner example: simple code, Monte Carlo returns, visible learning progress.
You’ll see clear variance: sometimes it learns quickly, other runs struggle — that’s expected for a pure policy gradient.
Example of Actor-Critic in Action — Policy Gradient vs Actor-Critic
PPO on continuous control tasks (MuJoCo) or A2C on Atari: actor-critic methods often converge faster and more reliably.
In practice, modern implementations of PPO (actor-critic with clipped objectives) are a go-to for balancing simplicity, performance, and robustness.
Conclusion: Policy Gradient vs Actor-Critic
Both approaches share a common goal: teach an agent to make better decisions. The difference is how they get to that goal.
Policy Gradient: simple, direct, unbiased but noisy and sample hungry.
Actor-Critic: mixes policy learning with value estimation, reducing variance and improving sample efficiency at the cost of more components.
If you’re experimenting or learning, start simple. If you’re solving production problems or complex simulations, prefer actor-critic families like PPO or SAC.
FAQs on Policy Gradient vs Actor-Critic
Is actor-critic better than policy gradient?
No universal “better” — actor-critic tends to be more practical for most real-world tasks because it lowers variance and improves sample efficiency. But policy gradient methods are conceptually simpler and useful for learning and research. Use actor-critic if you need stability and efficiency.
Why is actor-critic more stable than policy gradient?
Because the critic provides a baseline or advantage estimate that reduces variance in the policy gradient estimate. Instead of relying solely on full returns, actor-critic uses bootstrapped TD errors (or advantages), which provides a smoother, less noisy signal.
Which is easier to implement: policy gradient or actor-critic?
Policy gradients (like REINFORCE) are simpler to implement and good for educational purposes. Actor-critic adds complexity (a value network, extra loss terms), but modern libraries and tutorials make it approachable.
Can actor-critic be considered a type of policy gradient method?
Yes. Actor-critic methods are essentially a form of policy gradient where the gradient uses a learned value function (critic) to compute advantages. So they belong to the broader policy gradient family.
What are real-world applications of actor-critic vs policy gradient?
Policy Gradient: proof-of-concept experiments, continuous action prototypes, teaching concepts.
Actor-Critic: robotics control, game-playing agents, autonomous driving simulations, and other tasks where sample efficiency and stability matter.
Quick Practical Checklist (If you want to pick now)
Want something simple and educational → Policy Gradient (REINFORCE).
Need performance, stability, and sample efficiency → Actor-Critic (PPO/A2C/SAC).
Working with continuous actions and want robust results → SAC or DDPG/TD3 (actor-critic variants).
Want reliable, general-purpose performance across tasks → PPO (actor-critic, practical and widely used).
If you want, I can:
Draft a full code comparison (REINFORCE vs A2C/PPO) you can run and compare, or
Create an infographic summarizing the comparison for social sharing, or
Produce a step-by-step tutorial that demonstrates both approaches on the same environment (CartPole or MountainCar).
Which of these would you like next?