Solving Deep Reinforcement Learning Benchmarks with Linear Policy Networks

Although Deep Reinforcement Learning (DRL) methods can learn effective policies for challenging problems such as Atari games and robotics tasks, algorithms are complex and training times are often long. This study investigates how evolution strategies (ES) perform compared to gradient-based deep reinforcement learning methods. We use ES to optimize the weights of a neural network via neuroevolution, performing direct policy search. We benchmark both regular networks and policy networks consisting of a single linear layer from observations to actions; for …

algorithms atari games benchmarks cs.ai cs.lg evolution games gradient learn linear networks policy reinforcement reinforcement learning robotics strategies study tasks training