15 citations · 16 across the 20 of their papers we have counts for
10 papers · 1 filter
Risk-Averse Constrained Reinforcement Learning with Optimized Certainty Equivalents
Jane H. Lee, Baturay Saglam, Spyridon Pougkakiotis +2
Constrained optimization provides a common framework for dealing with conflicting objectives in reinforcement learning (RL). In most of these settings, the objectives (and constrai…
Compatible Gradient Approximations for Actor-Critic Algorithms
Baturay Saglam, Dionysis Kalogerias
Deterministic policy gradient algorithms are foundational for actor-critic methods in controlling continuous systems, yet they often encounter inaccuracies due to their dependence…
Deep Intrinsically Motivated Exploration in Continuous Control
Baturay Saglam, Suleyman S. Kozat
In continuous control, exploration is often performed through undirected strategies in which parameters of the networks or selected actions are perturbed by random noise. Although…
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…
Estimation Error Correction in Deep Reinforcement Learning for Deterministic Actor-Critic Methods
Baturay Saglam, Enes Duran, Dogan C. Cicek +2
In value-based deep reinforcement learning methods, approximation of value functions induces overestimation bias and leads to suboptimal policies. We show that in deep actor-critic…
Parameter-free Reduction of the Estimation Bias in Deep Reinforcement Learning for Deterministic Policy Gradients
Baturay Saglam, Furkan Burak Mutlu, Dogan Can Cicek +1
Approximation of the value functions in value-based deep reinforcement learning induces overestimation bias, resulting in suboptimal policies. We show that when the reinforcement s…