I’m working on a reinforcement learning project and running into some weird performance problems that don’t make sense to me.
My setup:
Using a cloud GPU instance with NVIDIA K80
Everything is pre-configured with CUDA and cuDNN
Installed keras-rl and OpenAI gym from scratch
Running the basic CartPole DQN example without visualization
The problem:
The GPU is only using about 20% of its capacity and I’m only getting around 100 training steps per second. What’s really frustrating is that my laptop CPU (Intel i7-8750H) actually runs the same code 3 times faster than this expensive GPU setup.
I’ve checked the usual suspects like CPU usage, RAM consumption, and disk I/O but everything looks normal there. The bottleneck seems to be somewhere else but I can’t figure out where.
Has anyone else experienced this kind of performance issue with reinforcement learning on GPU? Any ideas what might be causing the GPU to be so underutilized?
yep, that’s just how it works. keras-rl does these sync ops that really hurt GPU perf - each step is waiting for the last to finish. plus, cartpole’s samples are super tiny, like micro-batches, which wrecks gpu throughput. try increasing your update rate or switch to stable-baselines3 - it batches gpu better.
CartPole is a terrible GPU benchmark. The DQN network is just 2-3 dense layers with 64-128 neurons each - nowhere near enough to max out a K80. You’re spending more time moving data between CPU and GPU than actually computing anything. The K80’s pretty old now and has crappy memory bandwidth compared to newer cards. For simple stuff like CartPole, modern CPUs often beat older GPUs because there’s way less overhead. Want to see real GPU benefits? Try Atari environments with conv nets or crank up your batch size. Had the same problem until I switched to complex environments - then the GPU advantage was night and day.
Yeah, this is classic RL vs GPU mismatch. CartPole generates experiences one at a time, creating tiny computation chunks that can’t use GPU cores efficiently. The K80’s old architecture makes this worse - it’s built for massive workloads, not small neural nets. I hit the same wall moving from local to cloud GPUs. What worked for me: bump up the training frequency in keras-rl. Don’t update every step - accumulate experiences and update every 10-20 steps with bigger batches instead. The other killer is memory transfer overhead. Your small network weights keep bouncing between GPU memory and system RAM during training, creating bottlenecks you don’t get with CPU-only setups.
Yeah, this is super common with RL workloads, especially on K80s. RL has this nasty sequential dependency problem - each action needs the previous state, so you get pipeline stalls that GPUs absolutely hate. Unlike supervised learning where you can just slam massive batches through continuously, RL environments force you into step-by-step execution that leaves your GPU idle most of the time. I hit the same wall when testing different cloud providers. Vectorized environments helped a bit by running multiple CartPole instances in parallel, but honestly for simple control tasks the overhead isn’t worth it. K80s also have pretty high latency for small ops compared to newer cards. Try bumping up your replay buffer size and doing more frequent network updates to keep the GPU busier during training.