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Found 17 papers, 4 papers with code
Penalized Proximal Policy Optimization for Safe Reinforcement Learning
Safe reinforcement learning aims to learn the optimal policy while satisfying safety constraints, which is essential in real-world applications.
A Review of Safe Reinforcement Learning: Methods, Theory and Applications
To establish a good foundation for future research in this thread, in this paper, we provide a review for safe RL from the perspectives of methods, theory and applications.
CUP: A Conservative Update Policy Algorithm for Safe Reinforcement Learning
Although using bounds as surrogate functions to design safe RL algorithms have appeared in some existing works, we develop them at least three aspects: (i) We provide a rigorous theoretical analysis to extend the surrogate functions to generalized advantage estimator (GAE).
Secure Hybrid Beamforming for IRS-Assisted Millimeter Wave Systems
In this paper, we investigate the secure beamforming design in an intelligent reflection surface (IRS) assisted millimeter wave (mmWave) system, where the hybrid beamforming (HB) and the passive beamforming (PB) are employed by the transmitter and the IRS, respectively.
Thompson Sampling for Unimodal Bandits
In this paper, we propose a Thompson Sampling algorithm for \emph{unimodal} bandits, where the expected reward is unimodal over the partially ordered arms.
On Convergence of Gradient Expected Sarsa($λ$)
We study the convergence of $\mathtt{Expected~Sarsa}(\lambda)$ with linear function approximation.
Sample Complexity of Policy Gradient Finding Second-Order Stationary Points
However, due to the inherent non-concavity of its objective, convergence to a first-order stationary point (FOSP) can not guarantee the policy gradient methods finding a maximal point.
Gradient Q$(σ, λ)$: A Unified Algorithm with Function Approximation for Reinforcement Learning
To address above problem, we propose a GQ$(\sigma,\lambda)$ that extends tabular Q$(\sigma,\lambda)$ with linear function approximation.
FiDi-RL: Incorporating Deep Reinforcement Learning with Finite-Difference Policy Search for Efficient Learning of Continuous Control
Alternatively, derivative-based methods treat the optimization process as a blackbox and show robustness and stability in learning continuous control tasks, but not data efficient in learning.
Policy Optimization with Stochastic Mirror Descent
Improving sample efficiency has been a longstanding goal in reinforcement learning.
Expected Sarsa($λ$) with Control Variate for Variance Reduction
In this paper, for reducing the variance, we introduce control variate technique to $\mathtt{Expected}$ $\mathtt{Sarsa}$($\lambda$) and propose a tabular $\mathtt{ES}$($\lambda$)-$\mathtt{CV}$ algorithm.
TBQ($σ$): Improving Efficiency of Trace Utilization for Off-Policy Reinforcement Learning
However, existing off-policy learning methods based on probabilistic policy measurement are inefficient when utilizing traces under a greedy target policy, which is ineffective for control problems.
Beetle Swarm Optimization Algorithm:Theory and Application
In this paper, a new meta-heuristic algorithm, called beetle swarm optimization algorithm, is proposed by enhancing the performance of swarm optimization through beetle foraging principles.
Qualitative Measurements of Policy Discrepancy for Return-Based Deep Q-Network
In this paper, we propose a general framework to combine DQN and most of the return-based reinforcement learning algorithms, named R-DQN.
Texture Mapping for 3D Reconstruction With RGB-D Sensor
Acquiring realistic texture details for 3D models is important in 3D reconstruction.
A Unified Approach for Multi-step Temporal-Difference Learning with Eligibility Traces in Reinforcement Learning
Results show that, with an intermediate value of $\sigma$, $Q(\sigma ,\lambda)$ creates a mixture of the existing algorithms that can learn the optimal value significantly faster than the extreme end ($\sigma=0$, or $1$).
Distinguishing the Indistinguishable: Exploring Structural Ambiguities via Geodesic Context
A perennial problem in structure from motion (SfM) is visual ambiguity posed by repetitive structures.
