15 citations · 16 across the 7 of their papers we have counts for
8 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…
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…
Off-Policy Correction for Deep Deterministic Policy Gradient Algorithms via Batch Prioritized Experience Replay
Dogan C. Cicek, Enes Duran, Baturay Saglam +2
The experience replay mechanism allows agents to use the experiences multiple times. In prior works, the sampling probability of the transitions was adjusted according to their imp…