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 10251–10300 of 15113 papers

TitleStatusHype
Unsupervised Paraphrasing via Deep Reinforcement Learning—0
Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions—0
Bidirectional Model-based Policy OptimizationCode1
Discount Factor as a Regularizer in Reinforcement LearningCode0
Variational Policy Gradient Method for Reinforcement Learning with General Utilities—0
Reward Machines for Cooperative Multi-Agent Reinforcement LearningCode1
A Unifying View of Optimism in Episodic Reinforcement Learning—0
An Autonomous Free Airspace En-route Controller using Deep Reinforcement Learning Techniques—0
A Conceptual Framework for Externally-influenced Agents: An Assisted Reinforcement Learning Review—0
Hedging using reinforcement learning: Contextual k-Armed Bandit versus Q-learning—0
Temporal-Logic-Based Reward Shaping for Continuing Reinforcement Learning Tasks—0
Strategies for Using Proximal Policy Optimization in Mobile Puzzle Games—0
Learning "What-if" Explanations for Sequential Decision-Making—0
Safe Reinforcement Learning with Mixture Density Network: A Case Study in Autonomous Highway Driving—0
Decentralized Deep Reinforcement Learning for Network Level Traffic Signal Control—0
BOSH: Bayesian Optimization by Sampling Hierarchically—0
ε-BMC: A Bayesian Ensemble Approach to Epsilon-Greedy Exploration in Model-Free Reinforcement LearningCode0
Learning to search efficiently for causally near-optimal treatmentsCode0
Deep reinforcement learning driven inspection and maintenance planning under incomplete information and constraints—0
Human-centered collaborative robots with deep reinforcement learning—0
Verifiably Safe Exploration for End-to-End Reinforcement LearningCode1
Robust Inverse Reinforcement Learning under Transition Dynamics MismatchCode0
Semantic Guidance of Dialogue Generation with Reinforcement Learning—0
Group Equivariant Deep Reinforcement LearningCode0
Adaptive Discretization for Model-Based Reinforcement LearningCode0
Falsification-Based Robust Adversarial Reinforcement Learning—0
Student-Teacher Curriculum Learning via Reinforcement Learning: Predicting Hospital Inpatient Admission Location—0
Sequential Transfer in Reinforcement Learning with a Generative Model—0
Personalization of Hearing Aid Compression by Human-In-Loop Deep Reinforcement Learning—0
UAV Path Planning for Wireless Data Harvesting: A Deep Reinforcement Learning ApproachCode1
Reinforcement Learning based Control of Imitative Policies for Near-Accident DrivingCode1
A Generalized Reinforcement Learning Algorithm for Online 3D Bin-Packing—0
Interaction-limited Inverse Reinforcement Learning—0
Convex Regularization in Monte-Carlo Tree Search—0
Debiased Contrastive LearningCode1
Developing cooperative policies for multi-stage tasks—0
Composing Elementary Discourse Units in Abstractive Summarization—0
Zero-shot Text Classification via Reinforced Self-training—0
Meta-Reinforced Multi-Domain State Generator for Dialogue Systems—0
Testing match-3 video games with Deep Reinforcement Learning—0
MDP Homomorphic Networks: Group Symmetries in Reinforcement LearningCode1
Model-based Reinforcement Learning: A Survey—0
Enforcing Almost-Sure Reachability in POMDPsCode0
Accelerating Reinforcement Learning Agent with EEG-based Implicit Human Feedback—0
Evaluating the Performance of Reinforcement Learning AlgorithmsCode1
Deep reinforcement learning approach to MIMO precoding problem: Optimality and Robustness—0
Dynamic Regret of Policy Optimization in Non-stationary Environments—0
Deep Feature Space: A Geometrical PerspectiveCode0
Distributed Deep Reinforcement Learning for Intelligent Load Scheduling in Residential Smart Grids—0
Empirically Verifying Hypotheses Using Reinforcement Learning—0
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Benchmark Results

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