Reinforcement Learning Without Gradients

Reinforcement Learning Without Gradients refers to a class of reinforcement learning (RL) algorithms that optimize policies or value functions without using gradient-based optimization methods like backpropagation. Instead, these methods rely on techniques such as evolutionary strategies, random search, or other derivative-free optimization approaches to explore the parameter space and improve performance. This approach is particularly useful in environments where gradient computation is infeasible, noisy, or computationally expensive.

Also known as: Gradient-Free Reinforcement Learning, Derivative-Free RL, Evolutionary Reinforcement Learning, RL without gradients, Non-gradient RL

Why learn Reinforcement Learning Without Gradients?

Developers should learn this concept when working in RL scenarios where gradient-based methods fail due to non-differentiable environments, high noise, or when seeking robustness to local optima. It is applicable in areas like robotics control, game AI, and optimization problems where traditional deep RL struggles with stability or efficiency. For example, in training agents for complex simulations or real-world systems with sparse rewards, gradient-free methods can offer more reliable convergence.

Learning Resources

• docs: Evolution Strategies as a Scalable Alternative to Reinforcement Learning
• tutorial: OpenAI Spinning Up: Introduction to RL Algorithms
• course: Coursera: Reinforcement Learning Specialization
• docs: DeepMind: Reinforcement Learning Without Gradients