Simple random search provides a competitive approach to reinforcement learning (1803.07055v1)

Abstract: A common belief in model-free reinforcement learning is that methods based on random search in the parameter space of policies exhibit significantly worse sample complexity than those that explore the space of actions. We dispel such beliefs by introducing a random search method for training static, linear policies for continuous control problems, matching state-of-the-art sample efficiency on the benchmark MuJoCo locomotion tasks. Our method also finds a nearly optimal controller for a challenging instance of the Linear Quadratic Regulator, a classical problem in control theory, when the dynamics are not known. Computationally, our random search algorithm is at least 15 times more efficient than the fastest competing model-free methods on these benchmarks. We take advantage of this computational efficiency to evaluate the performance of our method over hundreds of random seeds and many different hyperparameter configurations for each benchmark task. Our simulations highlight a high variability in performance in these benchmark tasks, suggesting that commonly used estimations of sample efficiency do not adequately evaluate the performance of RL algorithms.

• The paper introduces ARS, a method that uses reward scaling, state normalization, and selective perturbations to achieve competitive sample efficiency in reinforcement learning.
• The approach employs simple linear policies to outperform complex techniques on MuJoCo locomotion tasks while reducing computational overhead.
• Empirical results demonstrate ARS's robustness and efficiency, prompting a re-evaluation of random search techniques in the design of RL algorithms.

Evaluation of Simple Random Search in Reinforcement Learning

In the paper titled "Simple random search provides a competitive approach to reinforcement learning," the authors present a compelling paper challenging the prevailing belief that random search in policy parameter space is less sample-efficient compared to exploration in action space. The research unfolds a random search method designed for training static, linear policies applicable to continuous control problems, achieving state-of-the-art sample efficiency on MuJoCo locomotion tasks. Their method coins as Augmented Random Search (ARS) and is notable for its simplicity and efficiency.

Summary of Contributions

The paper's core contribution is the introduction of ARS, which augments basic random search with several features to make it applicable to reinforcement learning (RL) problems, particularly in continuous control contexts. The enhancements include:

1. Reward Scaling: Update steps are scaled by the standard deviation of rewards to handle variations and prevent large or small updates irrespective of the rewards scale.
2. State Normalization: States are normalized using online estimates to ensure isotropic exploration across different state dimensions, enhancing random search's effectiveness.
3. Top-Performing Directions: Instead of averaging over all perturbations, ARS exploits only top-performing directions, reducing the adverse effects of noise in reward estimation.

Empirical Evaluation

The authors present an extensive empirical evaluation of ARS:

• MuJoCo Benchmark Tasks: ARS achieves strong performance across all MuJoCo locomotion tasks, including complex environments like Humanoid, where it reaches rewards of 11,600, outperforming other solutions.
• Comparative Analysis: ARS achieves or surpasses the sample efficiency of existing methods, including Natural Gradient, TRPO, and Evolution Strategies, using linear policies without requiring complex architectures such as neural networks.
• Sensitivity and Variability: The researchers conduct comprehensive sensitivity analyses regarding hyperparameter choices and randomness, affirming ARS's robustness and variance across multiple experimental trials.
• Time Efficiency: Employing a parallelized approach over 48 CPUs, ARS showcases competitive wall-clock time performance, demonstrating significant computational efficiency compared to popular methods like Evolution Strategies.

Theoretical and Practical Implications

The findings suggest fundamental reconsideration of random search's role in reinforcement learning. This paper underlines how parameter-space exploration, with suitable enhancements, can achieve comparable or superior results to more sophisticated action-space exploration techniques traditionally favored in RL literature.

Furthermore, the empirical results promote linear policies as a practical, reduced-complexity baseline for continuous control problems, challenging the prevailing inclination towards deep networks for all tasks.

Speculations on Future Developments

The implications of this work extend to the broader AI field, hinting at potential paradigm shifts in model-free RL algorithm design. Future avenues might explore:

• Extending ARS to more complex policy structures while retaining computational efficiency.
• Integrating ARS with model-based approaches to leverage hybrid strategies combining model dynamics understanding with efficient policy optimization.
• Investigating theoretical properties further to formalize the observed empirical performance.

This research enriches the ongoing discourse on balancing simplicity and efficiency in RL, pushing boundaries on where and how random search methodologies are applied effectively. The paper presents compelling evidence to rethink assumptions about sample efficiency and computational dynamics in state-of-the-art reinforcement learning practices.