20,926 research outputs found
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
Traffic Light Control Using Deep Policy-Gradient and Value-Function Based Reinforcement Learning
Recent advances in combining deep neural network architectures with
reinforcement learning techniques have shown promising potential results in
solving complex control problems with high dimensional state and action spaces.
Inspired by these successes, in this paper, we build two kinds of reinforcement
learning algorithms: deep policy-gradient and value-function based agents which
can predict the best possible traffic signal for a traffic intersection. At
each time step, these adaptive traffic light control agents receive a snapshot
of the current state of a graphical traffic simulator and produce control
signals. The policy-gradient based agent maps its observation directly to the
control signal, however the value-function based agent first estimates values
for all legal control signals. The agent then selects the optimal control
action with the highest value. Our methods show promising results in a traffic
network simulated in the SUMO traffic simulator, without suffering from
instability issues during the training process
- …