RL
  • Introduction
  • Deep RL Course
    • Introduction
    • Intro to RL
      • MDP Definition
      • RL Objective
      • Structure of RL algorithms
      • Value functions and Q-functions
      • Types of RL algorithms
      • Comparison
    • Policy Gradient
      • Evaluate the PG
      • Intuition of PG
      • Reduce variance
      • Off-policy version PG
      • PG in practice
    • Actor-Critic Algorithms
      • Advantages
      • Policy evaluation
      • Discount factors
      • Actor-Critic in practice
      • Baselines
      • Other advantages
    • Value Function Methods
      • Policy iteration
      • Value iteration
      • Q iteration
      • Learning theory
    • Deep RL with Q-Function
      • Correlated samples and unstable target
      • The accuracy of Q-function
      • Continuous actions
    • Advanced Policy Gradient
    • Optimal Control and Planning
    • Model-Based RL
    • Advanced Model-Based RL
    • Model-Based RL and Policy Learning
    • Variational Inference and Generative Models
    • Reframing Control as an Inference Problem
    • Inverse Reinforcement Learning
    • Exploration
    • Transfer Learning
    • Multi-Task Learning
    • Meta Learning
    • RL Challenges
    • AutoML
  • Related Papers
    • Meta RL
  • Resources
    • Resources
    • Spinning Up by OpenAI
  • RL in practice
    • Policy gradients
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  1. Deep RL Course
  2. Actor-Critic Algorithms

Actor-Critic in practice

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Last updated 5 years ago

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Architecture design

+: simple & stable

- : no shared features between actor and critic

+: shared features

-: need more time for hyper-parameter tuning

In practice

Online version perhaps works best with a batch(e.g., parallel)

2 networks design
shares network design
parallel actor-critic