41,564 research outputs found
An FPGA-Based On-Device Reinforcement Learning Approach using Online Sequential Learning
DQN (Deep Q-Network) is a method to perform Q-learning for reinforcement
learning using deep neural networks. DQNs require a large buffer and batch
processing for an experience replay and rely on a backpropagation based
iterative optimization, making them difficult to be implemented on
resource-limited edge devices. In this paper, we propose a lightweight
on-device reinforcement learning approach for low-cost FPGA devices. It
exploits a recently proposed neural-network based on-device learning approach
that does not rely on the backpropagation method but uses OS-ELM (Online
Sequential Extreme Learning Machine) based training algorithm. In addition, we
propose a combination of L2 regularization and spectral normalization for the
on-device reinforcement learning so that output values of the neural network
can be fit into a certain range and the reinforcement learning becomes stable.
The proposed reinforcement learning approach is designed for PYNQ-Z1 board as a
low-cost FPGA platform. The evaluation results using OpenAI Gym demonstrate
that the proposed algorithm and its FPGA implementation complete a CartPole-v0
task 29.77x and 89.40x faster than a conventional DQN-based approach when the
number of hidden-layer nodes is 64
Batch Reinforcement Learning on the Industrial Benchmark: First Experiences
The Particle Swarm Optimization Policy (PSO-P) has been recently introduced
and proven to produce remarkable results on interacting with academic
reinforcement learning benchmarks in an off-policy, batch-based setting. To
further investigate the properties and feasibility on real-world applications,
this paper investigates PSO-P on the so-called Industrial Benchmark (IB), a
novel reinforcement learning (RL) benchmark that aims at being realistic by
including a variety of aspects found in industrial applications, like
continuous state and action spaces, a high dimensional, partially observable
state space, delayed effects, and complex stochasticity. The experimental
results of PSO-P on IB are compared to results of closed-form control policies
derived from the model-based Recurrent Control Neural Network (RCNN) and the
model-free Neural Fitted Q-Iteration (NFQ). Experiments show that PSO-P is not
only of interest for academic benchmarks, but also for real-world industrial
applications, since it also yielded the best performing policy in our IB
setting. Compared to other well established RL techniques, PSO-P produced
outstanding results in performance and robustness, requiring only a relatively
low amount of effort in finding adequate parameters or making complex design
decisions
Small batch deep reinforcement learning
In value-based deep reinforcement learning with replay memories, the batch
size parameter specifies how many transitions to sample for each gradient
update. Although critical to the learning process, this value is typically not
adjusted when proposing new algorithms. In this work we present a broad
empirical study that suggests {\em reducing} the batch size can result in a
number of significant performance gains; this is surprising, as the general
tendency when training neural networks is towards larger batch sizes for
improved performance. We complement our experimental findings with a set of
empirical analyses towards better understanding this phenomenon.Comment: Published at NeurIPS 202
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