• deep reinforcement learning
• continuous action space
• autonomus robot
• Artificial Intelligence
• Actor-Critic
• Machine learning

The autonomous mobile robot must be able to adapt its skills in order to react adequately in complex and dynamic environments. Different approaches can be used to handle these kinds of applications: map-based navigation or map-less navigation. This work aims to prove that robot can goes towards target position, detecting and avoiding obstacles based on low dimensional data directly from sensors without the knowledge of the environment map. This goal is achieved using Deep Reinforcement Learning approach by a virtual-to-real strategy; thus, networks are trained in simulations before being inferred in real world. DRL method is Actor-Critic method and the training process is the Deterministic Policy Gradient update process.