Is Bang-Bang Control All You Need? Solving Continuous Control with Bernoulli Policies
This work addresses benchmarking challenges in continuous control for RL researchers, but it is incremental as it builds on known phenomena and existing algorithms.
The paper tackles the problem of reinforcement learning for continuous control by investigating why trained agents often prefer actions at the boundaries of the action space, and finds that replacing Gaussian policies with Bernoulli distributions (bang-bang controllers) achieves state-of-the-art performance on several benchmarks.
Reinforcement learning (RL) for continuous control typically employs distributions whose support covers the entire action space. In this work, we investigate the colloquially known phenomenon that trained agents often prefer actions at the boundaries of that space. We draw theoretical connections to the emergence of bang-bang behavior in optimal control, and provide extensive empirical evaluation across a variety of recent RL algorithms. We replace the normal Gaussian by a Bernoulli distribution that solely considers the extremes along each action dimension - a bang-bang controller. Surprisingly, this achieves state-of-the-art performance on several continuous control benchmarks - in contrast to robotic hardware, where energy and maintenance cost affect controller choices. Since exploration, learning,and the final solution are entangled in RL, we provide additional imitation learning experiments to reduce the impact of exploration on our analysis. Finally, we show that our observations generalize to environments that aim to model real-world challenges and evaluate factors to mitigate the emergence of bang-bang solutions. Our findings emphasize challenges for benchmarking continuous control algorithms, particularly in light of potential real-world applications.