SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3001–3050 of 15113 papers

TitleStatusHype
A Hierarchical Deep Reinforcement Learning Framework for 6-DOF UCAV Air-to-Air Combat—0
Asynchronous Distributed Reinforcement Learning for LQR Control via Zeroth-Order Block Coordinate Descent—0
Deep Learning of Robotic Tasks without a Simulator using Strong and Weak Human Supervision—0
DeepLine: AutoML Tool for Pipelines Generation using Deep Reinforcement Learning and Hierarchical Actions Filtering—0
Asynchronous Deep Double Duelling Q-Learning for Trading-Signal Execution in Limit Order Book Markets—0
Asynchronous Coagent Networks—0
A Hierarchical Bayesian Approach to Inverse Reinforcement Learning with Symbolic Reward Machines—0
Towards Understanding Asynchronous Advantage Actor-critic: Convergence and Linear Speedup—0
Asynchronous Advantage Actor-Critic Agent for Starcraft II—0
Adapting the Function Approximation Architecture in Online Reinforcement Learning—0
A Reinforcement Learning Perspective on the Optimal Control of Mutation Probabilities for the (1+1) Evolutionary Algorithm: First Results on the OneMax Problem—0
DeepLTL: Learning to Efficiently Satisfy Complex LTL Specifications for Multi-Task RL—0
Asynchronous Actor-Critic for Multi-Agent Reinforcement Learning—0
Asymptotics of Reinforcement Learning with Neural Networks—0
A Heuristically Assisted Deep Reinforcement Learning Approach for Network Slice Placement—0
Asymptotics of Language Model Alignment—0
Asymptotic Instance-Optimal Algorithms for Interactive Decision Making—0
A Guiding Principle for Causal Decision Problems—0
Adapting the Exploration Rate for Value-of-Information-Based Reinforcement Learning—0
Asymptotic Bias of Stochastic Gradient Search—0
Asymptotically Efficient Off-Policy Evaluation for Tabular Reinforcement Learning—0
A Guider Network for Multi-Dual Learning—0
Asymmetric REINFORCE for off-Policy Reinforcement Learning: Balancing positive and negative rewards—0
Asymmetric Actor Critic for Image-Based Robot Learning—0
A Greedy Approximation of Bayesian Reinforcement Learning with Probably Optimistic Transition Model—0
Adapting Surprise Minimizing Reinforcement Learning Techniques for Transactive Control—0
Accounting for the Sequential Nature of States to Learn Features for Reinforcement Learning—0
Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction—0
Deep Page-Level Interest Network in Reinforcement Learning for Ads Allocation—0
Adapting Sampling Interval of Sensor Networks Using On-Line Reinforcement Learning—0
A Survey on Transformers in Reinforcement Learning—0
A Survey on Traffic Signal Control Methods—0
A gray-box approach for curriculum learning—0
A2: Extracting Cyclic Switchings from DOB-nets for Rejecting Excessive Disturbances—0
A Survey on the Role of Artificial Intelligence and Machine Learning in 6G-V2X Applications—0
A Graph Policy Network Approach for Volt-Var Control in Power Distribution Systems—0
Adapting Behaviour via Intrinsic Reward: A Survey and Empirical Study—0
A Survey on Reinforcement Learning Security with Application to Autonomous Driving—0
A Survey on Reinforcement Learning Methods in Character Animation—0
A Graph Neural Network-Based QUBO-Formulated Hamiltonian-Inspired Loss Function for Combinatorial Optimization using Reinforcement Learning—0
The Principle of Unchanged Optimality in Reinforcement Learning Generalization—0
Deep Learning Interference Cancellation in Wireless Networks—0
A Survey on Reinforcement Learning in Aviation Applications—0
A Survey on Reinforcement Learning for Recommender Systems—0
A Graphical Approach to State Variable Selection in Off-policy Learning—0
A Survey on Reinforcement Learning for Combinatorial Optimization—0
A Survey on Reinforcement Learning-Aided Caching in Mobile Edge Networks—0
A Graph-based Adversarial Imitation Learning Framework for Reliable & Realtime Fleet Scheduling in Urban Air Mobility—0
Adapting Auxiliary Losses Using Gradient Similarity—0
A Survey on Recent Advances and Challenges in Reinforcement Learning Methods for Task-Oriented Dialogue Policy Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified