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 4601–4625 of 15113 papers

TitleStatusHype
Neural Packing: from Visual Sensing to Reinforcement Learning—0
Reinforcement learning with non-ergodic reward increments: robustness via ergodicity transformationsCode0
Reaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learning—0
Uncertainty-aware transfer across tasks using hybrid model-based successor feature reinforcement learning—0
Building Persona Consistent Dialogue Agents with Offline Reinforcement LearningCode0
Leveraging Topological Maps in Deep Reinforcement Learning for Multi-Object Navigation—0
Deep Reinforcement Learning with Explicit Context Representation—0
LgTS: Dynamic Task Sampling using LLM-generated sub-goals for Reinforcement Learning Agents—0
A Framework for Empowering Reinforcement Learning Agents with Causal Analysis: Enhancing Automated Cryptocurrency Trading—0
Hybrid Reinforcement Learning for Optimizing Pump Sustainability in Real-World Water Distribution Networks—0
Exploration with Principles for Diverse AI Supervision—0
Leveraging Optimal Transport for Enhanced Offline Reinforcement Learning in Surgical Robotic Environments—0
A Lightweight Calibrated Simulation Enabling Efficient Offline Learning for Optimal Control of Real BuildingsCode0
Learning RL-Policies for Joint Beamforming Without Exploration: A Batch Constrained Off-Policy ApproachCode0
Discerning Temporal Difference Learning—0
Novelty Detection in Reinforcement Learning with World Models—0
Dealing with uncertainty: balancing exploration and exploitation in deep recurrent reinforcement learningCode0
Virtual Augmented Reality for Atari Reinforcement LearningCode0
Reinforcement Learning-based Knowledge Graph Reasoning for Explainable Fact-checking—0
Off-Policy Evaluation for Human Feedback—0
Online RL in Linearly q^π-Realizable MDPs Is as Easy as in Linear MDPs If You Learn What to Ignore—0
Reinforcement Learning in a Safety-Embedded MDP with Trajectory Optimization—0
Spectral Entry-wise Matrix Estimation for Low-Rank Reinforcement Learning—0
Scalable Semantic Non-Markovian Simulation Proxy for Reinforcement Learning—0
Bi-Level Offline Policy Optimization with Limited Exploration—0
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Benchmark Results

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