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 1451–1475 of 15113 papers

TitleStatusHype
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
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
Integrating Reinforcement Learning and Large Language Models for Crop Production Process Management Optimization and Control through A New Knowledge-Based Deep Learning Paradigm—0
Improving Generalization on the ProcGen Benchmark with Simple Architectural Changes and ScaleCode0
Meta-Reinforcement Learning with Universal Policy Adaptation: Provable Near-Optimality under All-task Optimum Comparator—0
Transformers as Game Players: Provable In-context Game-playing Capabilities of Pre-trained Models—0
Generalization of Compositional Tasks with Logical Specification via Implicit Planning—0
Reinforcement Learning in Hyperbolic Spaces: Models and Experiments—0
ActSafe: Active Exploration with Safety Constraints for Reinforcement Learning—0
SAPIENT: Mastering Multi-turn Conversational Recommendation with Strategic Planning and Monte Carlo Tree SearchCode0
Physical Simulation for Multi-agent Multi-machine Tending—0
Enhancing Multi-Step Reasoning Abilities of Language Models through Direct Q-Function OptimizationCode2
MAD-TD: Model-Augmented Data stabilizes High Update Ratio RL—0
Overcoming Slow Decision Frequencies in Continuous Control: Model-Based Sequence Reinforcement Learning for Model-Free ControlCode0
SOLD: Slot Object-Centric Latent Dynamics Models for Relational Manipulation Learning from Pixels—0
Can we hop in general? A discussion of benchmark selection and design using the Hopper environment—0
Reinforcement Learning for Control of Non-Markovian Cellular Population DynamicsCode0
Drama: Mamba-Enabled Model-Based Reinforcement Learning Is Sample and Parameter EfficientCode1
Words as Beacons: Guiding RL Agents with High-Level Language Prompts—0
Exploring Natural Language-Based Strategies for Efficient Number Learning in Children through Reinforcement LearningCode0
Avoiding mode collapse in diffusion models fine-tuned with reinforcement learning—0
StablePrompt: Automatic Prompt Tuning using Reinforcement Learning for Large Language ModelsCode1
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Benchmark Results

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