Found 1301 results, showing the newest relevant preprints. Sort by relevancy only.Update me on new preprints

Placement Optimization of Aerial Base Stations with Deep Reinforcement Learning

To tackle this challenging problem, this paper applies the Deep Reinforcement Learning (DRL) method by 1) representing the state by a coverage bitmap to capture the spatial correlation of GUs/ABSs, whose dimension and associated neural network complexity is invariant with arbitrarily large N; and 2) designing the action... Expand abstract.
3 days ago
9/10 relevant
arXiv

Efficient decorrelation of features using Gramian in Reinforcement Learning

Learning good representations is a long standing problem in reinforcement learning (RL). Expand abstract.
3 days ago
9/10 relevant
arXiv

Bayesian Curiosity for Efficient Exploration in Reinforcement Learning

Balancing exploration and exploitation is a fundamental part of reinforcement learning, yet most state-of-the-art algorithms use a naive exploration protocol like $\epsilon$-greedy. Expand abstract.
3 days ago
10/10 relevant
arXiv

Inverse Cooperative and Non-Cooperative Dynamic Games Based on Maximum Entropy Inverse Reinforcement Learning

In this paper, we extend maximum entropy inverse reinforcement learning to the N-player case in order to solve inverse dynamic games with continuous-valued state and control spaces. Expand abstract.
4 days ago
9/10 relevant
arXiv

Inducing Cooperation via Team Regret Minimization based Multi-Agent Deep Reinforcement Learning

Existing value-factorized based Multi-Agent deep Reinforce-ment Learning (MARL) approaches are well-performing invarious multi-agent cooperative environment under thecen-tralized training and decentralized execution(CTDE) scheme,where all agents are trained together by the centralized valuenetwork and each agent execute its policy... Expand abstract.
4 days ago
7/10 relevant
arXiv

Missingness as Stability: Understanding the Structure of Missingness in Longitudinal EHR data and its Impact on Reinforcement Learning in Healthcare

In order to prepare longitudinal, irregularly sampled, clinical datasets for reinforcement learning algorithms, many researchers will resample the time series data to short, regular intervals and use last-observation-carried-forward (LOCF) imputation to fill in these gaps. Expand abstract.
6 days ago
10/10 relevant
arXiv

Reinforcement Learning from Imperfect Demonstrations under Soft Expert Guidance

In this paper, we study Reinforcement Learning from Demonstrations (RLfD) that improves the exploration efficiency of Reinforcement Learning (RL) by providing expert demonstrations. Expand abstract.
6 days ago
10/10 relevant
arXiv

Data-efficient Co-Adaptation of Morphology and Behaviour with Deep Reinforcement Learning

Our approach is based on recent advances in deep reinforcement learning, and specifically the soft actor critic algorithm. Expand abstract.
7 days ago
10/10 relevant
arXiv

Improved Exploration through Latent Trajectory Optimization in Deep Deterministic Policy Gradient

This approach leads to a symbiotic relationship between the deep reinforcement learning algorithm and the latent trajectory optimizer. Expand abstract.
7 days ago
4/10 relevant
arXiv

Empirical Study of Off-Policy Policy Evaluation for Reinforcement Learning

Given the increasing interest in deploying learning-based methods for safety-critical applications, many recent OPE methods have recently been proposed. Expand abstract.
7 days ago
7/10 relevant
arXiv