Sürekli kontrol için yeni derin pekiştirmeli öğrenme algoritmaları

Continuous control deep reinforcement learning (RL) algorithms are capable of learning complex and high-dimensional policies directly from raw sensory inputs. However, they often face challenges related to sample efficiency and exploration, which limit their practicality for real-world applications. In light of this, we introduce two novel techniques that enhance the performance of continuous control deep RL algorithms by refining their experience replay and exploration mechanisms. The first technique introduces a novel framework for sampling experiences in actor-critic methods. Specifically designed to stabilize and prevent divergence caused by Prioritized Experience Replay (PER), our framework effectively trains both actor and critic networks by striking a balance between temporal-difference error and policy gradient. Through both theoretical analysis and empirical investigations, we demonstrate that our framework is effective in improving the performance of continuous control deep RL algorithms. The second technique encompasses a directed exploration strategy that relies on intrinsic motivation. Drawing inspiration from established theories on animal motivational systems and adapting them to the actor-critic setting, our strategy showcases its effectiveness by generating exploratory behaviors that are both informative and diverse. It achieves this by maximizing the error of the value function and unifying the existing intrinsic exploration objectives in the literature. We evaluate the presented methods on various continuous control benchmarks and demonstrate that they outperform state-of-the-art methods while achieving new levels of performance in deep RL.