Evolution Strategies as a Scalable Alternative to Reinforcement Learning (1703.03864v2)

Abstract: We explore the use of Evolution Strategies (ES), a class of black box optimization algorithms, as an alternative to popular MDP-based RL techniques such as Q-learning and Policy Gradients. Experiments on MuJoCo and Atari show that ES is a viable solution strategy that scales extremely well with the number of CPUs available: By using a novel communication strategy based on common random numbers, our ES implementation only needs to communicate scalars, making it possible to scale to over a thousand parallel workers. This allows us to solve 3D humanoid walking in 10 minutes and obtain competitive results on most Atari games after one hour of training. In addition, we highlight several advantages of ES as a black box optimization technique: it is invariant to action frequency and delayed rewards, tolerant of extremely long horizons, and does not need temporal discounting or value function approximation.

• The paper demonstrates that ES can be scaled with minimal communication overhead using up to 1,000 parallel workers.
• The paper shows that ES achieves competitive performance on benchmark control tasks compared to traditional RL algorithms.
• The paper finds that ES reduces computational costs by eliminating gradient computations, enhancing efficiency for large-scale applications.

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

The paper by Tim Salimans, Jonathan Ho, Xi Chen, Szymon Sidor, and Ilya Sutskever titled "Evolution Strategies as a Scalable Alternative to Reinforcement Learning" investigates the applicability of Evolution Strategies (ES) as a viable and scalable approach for solving reinforcement learning (RL) problems. Presented in the document is an extensive evaluation of ES methodologies, contrasting them with traditional RL algorithms with a specific focus on scalability and computational efficiency.

Overview of Evolution Strategies (ES)

Evolution Strategies are a subset of black-box optimization techniques inspired by biological evolution. Unlike gradient-based optimization commonly used in RL, ES uses a population of solutions and iteratively improves upon them through mutation and selection mechanisms. The paper articulates the fundamentals of ES, particularly emphasizing its gradient-free nature, which mitigates some of the limitations inherent in conventional RL methods.

Key Findings

1. Scalability: The research demonstrates that ES can be parallelized with minimal communication overhead, making them particularly suitable for large-scale distributed computing environments. In the experiments conducted, the authors processed up to 1,000 parallel workers with only linear scaling in computational cost.
2. Performance: On several benchmark control tasks, including those sourced from the OpenAI Gym, ES-based algorithms show competitive performance with traditional RL algorithms like Proximal Policy Optimization (PPO). The paper presents detailed quantitative results showing how ES performs comparably even on high-dimensional and complex tasks.
3. Computation Efficiency: ES algorithms significantly reduce the computational burden associated with backpropagation and gradient computation. This property is notably advantageous in tasks where the gradient is either sparse or costly to compute.

Experimental Insights

The paper conducts a range of empirical experiments to evaluate the practical performance of ES. For example, in tasks such as Swimmer, Hopper, HalfCheetah, and Humanoid, the performance metrics (including reward and learning time) achieved by ES algorithms were on par with state-of-the-art RL algorithms. Furthermore, the robustness of ES to hyperparameter variations and the ease of tuning highlight its practical benefits.

Theoretical and Practical Implications

Theoretical implications of employing Evolution Strategies in RL domain revolve around their fundamentally different optimization strategy. Unlike methods reliant on stochastic gradient descent (SGD), ES optimizes the policy parameterization through the evaluation of entire trajectories, thus mitigating issues like gradient variance and susceptibility to local minima.

Practically, the findings elucidate the potential of ES in applications requiring large-scale parallelism. This capability can accelerate the learning process significantly in industry-scale problems, such as robotics control, automated trading systems, and large-scale game AI.

Future Directions

The paper signals several meaningful directions for future research. One notable avenue is the hybridization of ES with gradient-based methods to leverage the strengths of both. Additionally, there is room to further explore the integration of ES in other domains of machine learning, including supervised learning and unsupervised learning tasks.

Further investigation into the relationship between population size and algorithmic performance, more efficient exploration strategies, and adaptive mutation processes provides fertile ground for advancing the capabilities of ES.

In summary, this paper presents a thorough evaluation of Evolution Strategies as a scalable alternative to Reinforcement Learning, offering strong empirical results and suggesting significant practical advantages in terms of scalability and computation efficiency. The demonstrated benefits and proposed future research pathways underscore the potential of ES to play a prominent role in the advancement of AI and machine learning fields.