Open In App

We use cookies to ensure you have the best browsing experience on our website. By using our site, you acknowledge that you have read and understood our Cookie Policy & Privacy Policy

Actor-Critic Algorithm in Reinforcement Learning

Reinforcement learning (RL) stands as a pivotal component in the realm of artificial intelligence, enabling agents to learn optimal decision-making strategies through interaction with their environments.

Let's Dive into the actor-critic algorithm, a key concept in reinforcement learning, and learn how it can improve your machine learning models.

Table of Content

What is the Actor-Critic Algorithm?

The actor-critic algorithm is a type of reinforcement learning algorithm that combines aspects of both policy-based methods (Actor) and value-based methods (Critic). This hybrid approach is designed to address the limitations of each method when used individually.

In the actor-critic framework, an agent (the "actor") learns a policy to make decisions, and a value function (the "Critic") evaluates the actions taken by the Actor.

Simultaneously, the critic evaluates these actions by estimating their value or quality. This dual role allows the method to strike a balance between exploration and exploitation, leveraging the strengths of both policy and value functions.

Key Components of Reinforcement Learning

Before delving into the actor-critic method, it's crucial to understand the fundamental components of reinforcement learning (RL):

Roles of Actor and Critic

Key Terms in Actor Critic Algorithm

There are two key terms:

How Actor-Critic Algorithm works?

Actor Critic Algorithm Objective Function

Policy Gradient (Actor)

[Tex]\nabla_\theta J(\theta)\approx \frac{1}{N} \sum_{i=0}^{N} \nabla_\theta \log\pi_\theta (a_i|s_i)\cdot A(s_i,a_i) [/Tex]

Here,

Value Function Update (Critic)

[Tex]\nabla_w J(w) \approx \frac{1}{N}\sum_{i=1}^{N} \nabla_w (V_{w}(s_i)- Q_{w}(s_i , a_i))^2 [/Tex]

Here,

Update Rules

The update rules for the actor and critic involve adjusting their respective parameters using gradient ascent (for the actor) and gradient descent (for the critic).

Actor Update

[Tex] \theta_{t+1}= \theta_t + \alpha \nabla_\theta J(\theta_t) [/Tex]

Here,

Critic Update

[Tex]w_{t} = w_t -\beta \nabla_w J(w_t) [/Tex]

Here,

Advantage Function

The advantage function, [Tex]A(s,a) [/Tex], measures the advantage of taking action a in state s​ over the expected value of the state under the current policy.

[Tex]A(s,a)=Q(s,a)−V(s) [/Tex]

The advantage function, then, provides a measure of how much better or worse an action is compared to the average action.

These mathematical expressions highlight the essential computations involved in the Actor-Critic method. The actor is updated based on the policy gradient, encouraging actions with higher advantages, while the critic is updated to minimize the difference between the estimated value and the action-value.

A2C (Advantage Actor-Critic)

A2C (Advantage Actor-Critic) is a specific variant of the Actor-Critic algorithm that introduces the concept of the advantage function. This function measures how much better an action is compared to the average action in a given state. By incorporating this advantage information, A2C focuses the learning process on actions that have a significantly higher value than the typical action taken in that state.

While both leverage the actor-critic architecture, here's a key distinction between them:

Actor-Critic Algorithm Steps

The Actor-Critic algorithm combines these mathematical principles into a coherent learning framework. The algorithm involves:

  1. Initialization:
    • Initialize the policy parameters [Tex]\theta [/Tex](actor) and the value function parameters [Tex]\phi [/Tex] (critic).
  2. Interaction with the Environment:
    • The agent interacts with the environment by taking actions according to the current policy and receiving observations and rewards in return.
  3. Advantage Computation:
    • Compute the advantage function A(s,a) based on the current policy and value estimates.
  4. Policy and Value Updates:
    • Simultaneously update the actor's parameters[Tex](\theta)[/Tex] using the policy gradient. The policy gradient is derived from the advantage function and guides the actor to increase the probabilities of actions that lead to higher advantages.
    • Simultaneously update the critic's parameters [Tex](\phi)[/Tex]using a value-based method. This often involves minimizing the temporal difference (TD) error, which is the difference between the observed rewards and the predicted values.

The actor learns a policy, and the critic evaluates the actions taken by the actor. The actor is updated using the policy gradient, and the critic is updated using a value-based method. This combination allows for more stable and efficient learning in complex environments.

Training Agent: Actor-Critic Algorithm

Let's understand how the Actor-Critic algorithm works in practice. Below is an implementation of a simple Actor-Critic algorithm using TensorFlow and OpenAI Gym to train an agent in the CartPole environment.

Import Libraries

import numpy as np
import tensorflow as tf
import gym

2. Creating CartPole Environment

Create the CartPole environment using the gym.make() function from the Gym library because it provides a standardized and convenient way to interact with various reinforcement learning tasks.

# Create the CartPole Environment
env = gym.make('CartPole-v1')

3. Defining Actor and Critic Networks

# Define the actor and critic networks
actor = tf.keras.Sequential([
    tf.keras.layers.Dense(32, activation='relu'),
    tf.keras.layers.Dense(env.action_space.n, activation='softmax')
])

critic = tf.keras.Sequential([
    tf.keras.layers.Dense(32, activation='relu'),
    tf.keras.layers.Dense(1)
])

4. Defining Optimizers and Loss Functions

Adam optimizer is used for both the Actor and the Critic networks.

# Define optimizer and loss functions
actor_optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)
critic_optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)

5. Training Loop

# Main training loop
num_episodes = 1000
gamma = 0.99

for episode in range(num_episodes):
    state = env.reset()
    episode_reward = 0

    with tf.GradientTape(persistent=True) as tape:
        for t in range(1, 10000):  # Limit the number of time steps
            # Choose an action using the actor
            action_probs = actor(np.array([state]))
            action = np.random.choice(env.action_space.n, p=action_probs.numpy()[0])

            # Take the chosen action and observe the next state and reward
            next_state, reward, done, _ = env.step(action)

            # Compute the advantage
            state_value = critic(np.array([state]))[0, 0]
            next_state_value = critic(np.array([next_state]))[0, 0]
            advantage = reward + gamma * next_state_value - state_value

            # Compute actor and critic losses
            actor_loss = -tf.math.log(action_probs[0, action]) * advantage
            critic_loss = tf.square(advantage)

            episode_reward += reward

            # Update actor and critic
            actor_gradients = tape.gradient(actor_loss, actor.trainable_variables)
            critic_gradients = tape.gradient(critic_loss, critic.trainable_variables)
            actor_optimizer.apply_gradients(zip(actor_gradients, actor.trainable_variables))
            critic_optimizer.apply_gradients(zip(critic_gradients, critic.trainable_variables))

            if done:
                break

    if episode % 10 == 0:
        print(f"Episode {episode}, Reward: {episode_reward}")

env.close()

Output: 

Episode 0, Reward: 29.0
Episode 10, Reward: 14.0
Episode 20, Reward: 15.0
Episode 30, Reward: 15.0
Episode 40, Reward: 31.0
Episode 50, Reward: 20.0
Episode 60, Reward: 22.0
Episode 70, Reward: 8.0
Episode 80, Reward: 51.0
Episode 90, Reward: 14.0
Episode 100, Reward: 11.0
Episode 110, Reward: 25.0
Episode 120, Reward: 16.0
....

Advantages of Actor Critic Algorithm

The Actor-Critic method offer several advantages:

Advantage Actor Critic (A2C) vs. Asynchronous Advantage Actor Critic (A3C)

Asynchronous Advantage Actor-Critic (A3C) builds upon A2C by introducing parallelism.

In A2C, a single actor-critic pair interacts with the environment and updates its policy based on the experiences it gathers. However, A3C utilizes multiple actor-critic pairs operating simultaneously. Each pair interacts with a separate copy of the environment, collecting data independently. These experiences are then used to update a global actor-critic network.

Imagine training multiple agents simultaneously, each exploring a separate world. That's the core idea behind A3C (Asynchronous Advantage Actor-Critic). These agents, called "workers," independently learn from their experiences and update a central value function. This parallel approach allows A3C to explore the environment much faster than a single agent, leading to quicker learning.

A2C (Advantage Actor-Critic) is like A3C's simpler cousin. It uses the same core concept of actor-critic with an advantage function, but without the parallel workers. While A2C explores the environment less extensively, studies have shown it can achieve similar performance to A3C while being easier to implement and requiring less computational power.

Applications of Actor Critic Algorithm

The Actor-Critic algorithm's versatility extends its reach across a myriad of applications within the field of artificial intelligence. Some notable applications include:

Conclusion

In conclusion, the Actor-Critic algorithm emerges as a pivotal advancement in reinforcement learning, effectively addressing challenges faced by traditional RL algorithms.

Actor-Critic Algorithm in Reinforcement Learning -FAQs

What are the applications of Actor-Critic methods?

Is PPO an Actor-Critic algorithm?

Article Tags :
AI-ML-DS
Geeks Premier League
Machine Learning
Recommended Articles
1. Asynchronous Advantage Actor Critic (A3C) algorithm
2. ML | Reinforcement Learning Algorithm : Python Implementation using Q-learning
3. Genetic Algorithm for Reinforcement Learning : Python implementation
4. Epsilon-Greedy Algorithm in Reinforcement Learning
5. Upper Confidence Bound Algorithm in Reinforcement Learning
6. Reinforcement learning
7. SARSA Reinforcement Learning
8. Introduction to Thompson Sampling | Reinforcement Learning
9. Neural Logic Reinforcement Learning - An Introduction
10. Expected SARSA in Reinforcement Learning
11. Understanding Reinforcement Learning in-depth
12. Sparse Rewards in Reinforcement Learning
13. A Beginner's Guide to Deep Reinforcement Learning
14. On-policy vs off-policy methods Reinforcement Learning
15. Model-Free Reinforcement Learning: An Overview
16. What is Compatible Function Approximation Theorem in Reinforcement Learning?
17. Reinforcement learning from Human Feedback
18. Reinforcement Learning using PyTorch
19. The Role of Reinforcement Learning in Autonomous Systems
20. Introduction to Multi-Task Learning(MTL) for Deep Learning
21. Artificial intelligence vs Machine Learning vs Deep Learning
22. Difference Between Artificial Intelligence vs Machine Learning vs Deep Learning
23. Need of Data Structures and Algorithms for Deep Learning and Machine Learning
24. Fusion Learning - The One Shot Federated Learning
25. Machine Learning - Learning VS Designing