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 11001–11050 of 15113 papers

TitleStatusHype
Adaptive Dialog Policy Learning with Hindsight and User Modeling—0
Reinforcement Learning with Feedback Graphs—0
Safe Reinforcement Learning through Meta-learned Instincts—0
Robotic Arm Control and Task Training through Deep Reinforcement Learning—0
Reinforcement Learning for UAV Autonomous Navigation, Mapping and Target Detection—0
Gifting in multi-agent reinforcement learningCode0
A Survey on Dialog Management: Recent Advances and Challenges—0
Generalized Planning With Deep Reinforcement Learning—0
Discrete-to-Deep Supervised Policy LearningCode0
Formal Policy Synthesis for Continuous-Space Systems via Reinforcement Learning—0
Generalized Reinforcement Meta Learning for Few-Shot Optimization—0
Hierarchical Decomposition of Nonlinear Dynamics and Control for System Identification and Policy Distillation—0
Reward Constrained Interactive Recommendation with Natural Language Feedback—0
Multiagent Value Iteration Algorithms in Dynamic Programming and Reinforcement Learning—0
Setting up experimental Bell test with reinforcement learning—0
Noise Pollution in Hospital Readmission Prediction: Long Document Classification with Reinforcement Learning—0
Multi-agent Reinforcement Learning for Decentralized Stable Matching—0
Optimal Beam Association for High Mobility mmWave Vehicular Networks: Lightweight Parallel Reinforcement Learning Approach—0
Deep Reinforcement Learning for Intelligent Transportation Systems: A Survey—0
Enhancing Text-based Reinforcement Learning Agents with Commonsense Knowledge—0
Learning the Arrow of Time for Problems in Reinforcement Learning—0
AMRL: Aggregated Memory For Reinforcement Learning—0
Learning Heuristics for Quantified Boolean Formulas through Reinforcement Learning—0
Keep Doing What Worked: Behavior Modelling Priors for Offline Reinforcement Learning—0
Explain Your Move: Understanding Agent Actions Using Focused Feature SaliencyCode0
Is Long Horizon Reinforcement Learning More Difficult Than Short Horizon Reinforcement Learning?—0
Improving Robustness via Risk Averse Distributional Reinforcement Learning—0
Exploration in Reinforcement Learning with Deep Covering Options—0
Episodic Reinforcement Learning with Associative Memory—0
Learning Efficient Parameter Server Synchronization Policies for Distributed SGD—0
Synthesizing Programmatic Policies that Inductively Generalize—0
Model Based Reinforcement Learning for Atari—0
Model-based reinforcement learning for biological sequence design—0
Toward Evaluating Robustness of Deep Reinforcement Learning with Continuous Control—0
Posterior sampling for multi-agent reinforcement learning: solving extensive games with imperfect information—0
The Ingredients of Real World Robotic Reinforcement Learning—0
Reinforcement learning of minimalist grammars—0
Unsupervised Learning of KB Queries in Task-Oriented Dialogs—0
Towards Embodied Scene Description—0
Out-of-the-box channel pruned networks—0
Plan-Space State Embeddings for Improved Reinforcement Learning—0
DSAC: Distributional Soft Actor Critic for Risk-Sensitive Reinforcement Learning—0
GCN-RL Circuit Designer: Transferable Transistor Sizing with Graph Neural Networks and Reinforcement Learning—0
Delay-aware Resource Allocation in Fog-assisted IoT Networks Through Reinforcement Learning—0
Improving Factual Consistency Between a Response and Persona Facts—0
Breaking (Global) Barriers in Parallel Stochastic Optimization with Wait-Avoiding Group Averaging—0
Bootstrap Latent-Predictive Representations for Multitask Reinforcement Learning—0
Graph-based State Representation for Deep Reinforcement LearningCode0
Reduced-Dimensional Reinforcement Learning Control using Singular Perturbation Approximations—0
Whittle index based Q-learning for restless bandits with average reward—0
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Benchmark Results

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