Deep Reinforcement Learning for Real-Time Robotic Control in Dynamic Environments

Real-time robotic control in dynamic environments presents significant challenges due to the need for continuous adaptation and optimal decision-making under uncertainty. This research uses the application of Deep Reinforcement Learning (DRL) to achieve efficient and effective control in such settings. Deep Deterministic Policy Gradient (DDPG) algorithm has been employed, which is ideal for continuous action spaces, to train a robotic agent in a simulated environment. The proposed approach involves designing actor and critic networks, leveraging a replay buffer for experience replay, and utilizing focus networks to maintain training. The efficacy of our method has been demonstrated using the OpenAI Gym’s Pendulum-v1 environment, a standard benchmark for continuous control tasks. The findings indicate that the suggested DRL model learns robust control policies, enabling the robotic agent to perform tasks with high precision and adaptability. The trained agent’s performance is evaluated based on cumulative rewards, and the training process is visualized to provide insights into the learning dynamics. This research highlights the potential of DRL in advancing real-time robotic control and prepares the path for future research in more complex and authentic surroundings.