In this research, an optimization methodology was introduced for improving bipedal robot
locomotion controlled by reinforcement learning (RL) algorithms. Specifically, the study
focused on optimizing the Proximal Policy Optimization (PPO), Advantage Actor-Critic (A2C),
Soft Actor-Critic (SAC), and Twin Delayed Deep Deterministic Policy Gradients (TD3)
algorithms. The optimization process utilized the Tree-structured Parzen Estimator (TPE), a
Bayesian optimization technique. All RL algorithms were applied to the same environment …

In this study, reinforcement learning algorithms are compared in TORCS simulation
environment. In this simulation environment, the goal is to finish the track as soon as
possible by controlling the car. The agent decides actions by using highlevel observations
from the environment. For this goal, two reinforcement learning algorithms (Deep
Deterministic Policy Gradient (DDPG) and Deep Q Network (DQN)) are used and the results
are compared and analyzed. Since the action space is continuous, DDPG algorithm …