Actor critic algorithm - chunhualiao/public-docs GitHub Wiki

PPO

Why Does PPO Need Both the Policy Network and Value Network?

PPO requires both a policy network and a value network because it uses an actor-critic approach. Each network serves a specific role:

  • Policy Network (Actor): Determines the best action to take.
  • Value Network (Critic): Evaluates how good a state (or action) is.

This combination improves training stability and efficiency.

How Do the Policy and Value Networks Work Together?

PPO follows an actor-critic framework where the policy network (actor) and the value network (critic) interact in a feedback loop:

  1. Policy Network (Actor) Chooses an Action
    • Given a state $s_t$, the policy network (actor) outputs a probability distribution over possible actions:
     \pi_{\theta}(a_t | s_t)
  • It samples an action from this distribution.

  1. Environment Provides a Reward

    • The action is executed in the environment, and the agent receives a reward $r_t$.

  2. Value Network (Critic) Evaluates the State

    • The value network estimates the expected future rewards (called the state value function):
     V_{\phi}(s_t) \approx \mathbb{E} \left[ R_t \right]
  • This helps measure how good a state is and is used to compute the advantage function:
     A_t = R_t - V_{\phi}(s_t)

 where **$A_t$** tells us **how much better or worse an action is** compared to the expected return.
  1. Policy Updates Using the Advantage Function
    • The policy network is trained to maximize the probability of good actions using advantage-weighted updates:
     J(\theta) = \mathbb{E}_t \left[ \min \left( r_t(\theta) A_t, \text{clip}(r_t(\theta), 1-\epsilon, 1+\epsilon) A_t \right) \right]
  • The clipping mechanism ensures that updates do not become too aggressive.
  1. Value Network Updates
    • The value network is trained to minimize the error in its state value predictions using a mean squared error loss:
     L_V(\phi) = \mathbb{E}_t \left[ (V_{\phi}(s_t) - R_t)^2 \right]
  • This ensures that future advantage estimates are accurate.

Why Can't PPO Just Use a Policy Network?

Without a value network, PPO would rely only on policy gradients, leading to:

  • High variance: Policy gradients fluctuate significantly, making learning unstable.
  • Inefficient learning: Without an advantage function, the policy network updates could be noisy and unreliable.
  • Slower convergence: The critic helps refine learning by providing an extra learning signal.

Why Can't PPO Just Use a Value Network?

A value network alone (without a policy network) would turn PPO into value-based methods like DQN, which:

  • Struggle with continuous action spaces (since PPO is designed for both discrete and continuous control).
  • Require Q-value maximization, which can introduce instability.

Summary

Component Role Why It's Needed in PPO
Policy Network (Actor) Chooses actions Directly optimizes policy to take better actions
Value Network (Critic) Evaluates states Helps compute the advantage function for stable updates

PPO needs both networks because:

  • The policy network decides what to do (actor).
  • The value network evaluates decisions (critic).
  • The advantage function guides training, ensuring stable learning.

Would you like a code example showing how these networks interact? 🚀