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Found 6 papers, 3 papers with code
AdCraft: An Advanced Reinforcement Learning Benchmark Environment for Search Engine Marketing Optimization
We introduce AdCraft, a novel benchmark environment for the Reinforcement Learning (RL) community distinguished by its stochastic and non-stationary properties.
Membership Inference Attacks Against Temporally Correlated Data in Deep Reinforcement Learning
To address this gap, we propose an adversarial attack framework designed for testing the vulnerability of a state-of-the-art deep reinforcement learning algorithm to a membership inference attack.
A Survey of Exploration Methods in Reinforcement Learning
Exploration is an essential component of reinforcement learning algorithms, where agents need to learn how to predict and control unknown and often stochastic environments.
Locally Persistent Exploration in Continuous Control Tasks with Sparse Rewards
A major challenge in reinforcement learning is the design of exploration strategies, especially for environments with sparse reward structures and continuous state and action spaces.
Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control
We investigate and discuss: the significance of hyper-parameters in policy gradients for continuous control, general variance in the algorithms, and reproducibility of reported results.
Differentially Private Policy Evaluation
We present the first differentially private algorithms for reinforcement learning, which apply to the task of evaluating a fixed policy.
