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 10951–11000 of 15113 papers

TitleStatusHype
Improving Proactive Dialog Agents Using Socially-Aware Reinforcement Learning—0
Toward Simulating Environments in Reinforcement Learning Based Recommendations—0
Towards Infant Sleep-Optimized Driving: Synergizing Wearable and Vehicle Sensing in Intelligent Cruise Control—0
Towards Information-Seeking Agents—0
Towards Instance-Optimal Offline Reinforcement Learning with Pessimism—0
Towards Intelligent Pick and Place Assembly of Individualized Products Using Reinforcement Learning—0
Towards Interpretable Deep Reinforcement Learning Models via Inverse Reinforcement Learning—0
Towards intervention-centric causal reasoning in learning agents—0
Towards Interactive Reinforcement Learning with Intrinsic Feedback—0
Towards Inverse Reinforcement Learning for Limit Order Book Dynamics—0
Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models—0
Towards Learning Abstractions via Reinforcement Learning—0
Towards Learning-automation IoT Attack Detection through Reinforcement Learning—0
Towards Learning Controllable Representations of Physical Systems—0
Towards Learning Rubik's Cube with N-tuple-based Reinforcement Learning—0
Towards Learning to Play Piano with Dexterous Hands and Touch—0
Towards Learning to Speak and Hear Through Multi-Agent Communication over a Continuous Acoustic Channel—0
Towards Machines that Trust: AI Agents Learn to Trust in the Trust Game—0
Towards Measuring Goal-Directedness in AI Systems—0
Towards Minimax Optimality of Model-based Robust Reinforcement Learning—0
Towards Minimax Optimal Reinforcement Learning in Factored Markov Decision Processes—0
Towards Mixed Optimization for Reinforcement Learning with Program Synthesis—0
Towards model-free RL algorithms that scale well with unstructured data—0
Towards Modern Card Games with Large-Scale Action Spaces Through Action Representation—0
Towards Modular Algorithm Induction—0
Towards Modularity Optimization Using Reinforcement Learning to Community Detection in Dynamic Social Networks—0
Towards More Efficient, Robust, Instance-adaptive, and Generalizable Sequential Decision making—0
Towards More Theoretically-Grounded Particle Optimization Sampling for Deep Learning—0
Towards Multi-agent Reinforcement Learning for Wireless Network Protocol Synthesis—0
Towards Multi-Agent Reinforcement Learning using Quantum Boltzmann Machines—0
Towards Multi-Agent Reinforcement Learning driven Over-The-Counter Market Simulations—0
Towards Neural Machine Translation with Latent Tree Attention—0
Towards one-shot learning for rare-word translation with external experts—0
Towards on-sky adaptive optics control using reinforcement learning—0
Towards Optimal Differentially Private Regret Bounds in Linear MDPs—0
Towards Optimal District Heating Temperature Control in China with Deep Reinforcement Learning—0
Towards Optimal Energy Management Strategy for Hybrid Electric Vehicle with Reinforcement Learning—0
Towards Optimal Pricing of Demand Response -- A Nonparametric Constrained Policy Optimization Approach—0
Towards Optimizing Human-Centric Objectives in AI-Assisted Decision-Making With Offline Reinforcement Learning—0
Towards personalized human AI interaction - adapting the behavior of AI agents using neural signatures of subjective interest—0
Towards Physically Safe Reinforcement Learning under Supervision—0
Towards Physiologically Sensible Predictions via the Rule-based Reinforcement Learning Layer—0
Towards Playing Full MOBA Games with Deep Reinforcement Learning—0
Towards Practical Credit Assignment for Deep Reinforcement Learning—0
Towards Practical Deep Schedulers for Allocating Cellular Radio Resources—0
Towards practical reinforcement learning for tokamak magnetic control—0
Towards Quantum-Enabled 6G Slicing—0
Towards Reinforcement Learning for Pivot-based Neural Machine Translation with Non-autoregressive Transformer—0
Towards Resolving Unidentifiability in Inverse Reinforcement Learning—0
Towards robust and domain agnostic reinforcement learning competitions—0
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Benchmark Results

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