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 3426–3450 of 15113 papers

TitleStatusHype
Multi-objective Reinforcement Learning: A Tool for Pluralistic Alignment—0
Diffusion-Based Offline RL for Improved Decision-Making in Augmented ARC Task—0
Reinforcement Learning Based Bidding Framework with High-dimensional Bids in Power Markets—0
Multi-Objective-Optimization Multi-AUV Assisted Data Collection Framework for IoUT Based on Offline Reinforcement Learning—0
ILAEDA: An Imitation Learning Based Approach for Automatic Exploratory Data Analysis—0
Large Language Model-Enhanced Reinforcement Learning for Generic Bus Holding Control Strategies—0
Asymptotic Analysis of Sample-averaged Q-learning—0
DR-MPC: Deep Residual Model Predictive Control for Real-world Social Navigation—0
Action Gaps and Advantages in Continuous-Time Distributional Reinforcement Learning—0
Burning RED: Unlocking Subtask-Driven Reinforcement Learning and Risk-Awareness in Average-Reward Markov Decision Processes—0
Meta-Reinforcement Learning with Universal Policy Adaptation: Provable Near-Optimality under All-task Optimum Comparator—0
Generalization of Compositional Tasks with Logical Specification via Implicit Planning—0
Improving Generalization on the ProcGen Benchmark with Simple Architectural Changes and ScaleCode0
Integrating Reinforcement Learning and Large Language Models for Crop Production Process Management Optimization and Control through A New Knowledge-Based Deep Learning Paradigm—0
Transformers as Game Players: Provable In-context Game-playing Capabilities of Pre-trained Models—0
SAPIENT: Mastering Multi-turn Conversational Recommendation with Strategic Planning and Monte Carlo Tree SearchCode0
ActSafe: Active Exploration with Safety Constraints for Reinforcement Learning—0
Reinforcement Learning in Hyperbolic Spaces: Models and Experiments—0
Overcoming Slow Decision Frequencies in Continuous Control: Model-Based Sequence Reinforcement Learning for Model-Free ControlCode0
Reinforcement Learning for Control of Non-Markovian Cellular Population DynamicsCode0
Words as Beacons: Guiding RL Agents with High-Level Language Prompts—0
MAD-TD: Model-Augmented Data stabilizes High Update Ratio RL—0
SOLD: Slot Object-Centric Latent Dynamics Models for Relational Manipulation Learning from Pixels—0
Physical Simulation for Multi-agent Multi-machine Tending—0
Can we hop in general? A discussion of benchmark selection and design using the Hopper environment—0
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Benchmark Results

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