15 citations · 16 across the 8 of their papers we have counts for
9 papers
Enhancing Deep Deterministic Policy Gradients on Continuous Control Tasks with Decoupled Prioritized Experience Replay
Mehmet Efe Lorasdagi, Dogan Can Cicek, Furkan Burak Mutlu +1
Background: Deep Deterministic Policy Gradient-based reinforcement learning algorithms utilize Actor-Critic architectures, where both networks are typically trained using identical…
CUER: Corrected Uniform Experience Replay for Off-Policy Continuous Deep Reinforcement Learning Algorithms
Arda Sarp Yenicesu, Furkan B. Mutlu, Suleyman S. Kozat +1
The utilization of the experience replay mechanism enables agents to effectively leverage their experiences on several occasions. In previous studies, the sampling probability of t…
Actor Prioritized Experience Replay
Baturay Saglam, Furkan B. Mutlu, Dogan C. Cicek +1
A widely-studied deep reinforcement learning (RL) technique known as Prioritized Experience Replay (PER) allows agents to learn from transitions sampled with non-uniform probabilit…
Mitigating Off-Policy Bias in Actor-Critic Methods with One-Step Q-learning: A Novel Correction Approach
Baturay Saglam, Dogan C. Cicek, Furkan B. Mutlu +1
Compared to on-policy counterparts, off-policy model-free deep reinforcement learning can improve data efficiency by repeatedly using the previously gathered data. However, off-pol…
Safe and Robust Experience Sharing for Deterministic Policy Gradient Algorithms
Baturay Saglam, Dogan C. Cicek, Furkan B. Mutlu +1
Learning in high dimensional continuous tasks is challenging, mainly when the experience replay memory is very limited. We introduce a simple yet effective experience sharing mecha…
AWD3: Dynamic Reduction of the Estimation Bias
Dogan C. Cicek, Enes Duran, Baturay Saglam +3
Value-based deep Reinforcement Learning (RL) algorithms suffer from the estimation bias primarily caused by function approximation and temporal difference (TD) learning. This probl…