I'm trying to use reinforcement learning with live EEG measurements. However, just 2000 measurements/iterations take 16.6 minutes to measure and it seems I need at least 10 hours of live measurements before some kind of usable results.
Can you recommend ways to reduce the number of measurements and optimization iterations needed in reinforcement learning?
I have tried to keep neural network as small as possible so there are less parameters to learn.
To minimize the number of measurements or iterations required in deep reinforcement learning (RL), several strategies can be employed. Transfer learning is effective, leveraging pre-trained models to accelerate learning in new environments by transferring knowledge from related tasks. Prioritized experience replay prioritizes learning from more informative experiences, optimizing the use of collected data. Model-based RL incorporates a learned model of the environment, enabling simulations that reduce the need for real-world interactions. Curriculum learning starts with simpler tasks and gradually increases complexity, aiding faster convergence. Efficient exploration strategies, such as curiosity-driven exploration, focus on exploring novel and informative states to accelerate learning. Reward shaping involves designing more informative reward signals to guide the RL agent effectively. Batch learning uses larger batches of experiences to stabilize learning updates, while parallel training with multiple agents allows for more efficient gathering of experience and faster learning overall. These approaches collectively aim to streamline the RL process, reducing the time and computational resources required for achieving optimal performance.
You can use the active learning method to improve data efficiency in reinforcement learning. This technique involves querying the model for uncertainty estimates and selectively collecting data in areas where predictions are uncertain. By doing this, you can decrease the reliance on random exploration. However, t is important to consider that the optimal strategy will vary depending on your specific EEG task, available resources, and computational limitations. Therefore, it is recommended to experiment with different approaches in order to find the right balance between efficiency and performance in RL.
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