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 7126–7150 of 15113 papers

TitleStatusHype
Showing Your Offline Reinforcement Learning Work: Online Evaluation Budget Matters—0
Revisiting Design Choices in Offline Model-Based Reinforcement Learning—0
Learning to Centralize Dual-Arm Assembly—0
CheerBots: Chatbots toward Empathy and Emotionusing Reinforcement Learning—0
Designing Composites with Target Effective Young's Modulus using Reinforcement Learning—0
Arachnophobia Exposure Therapy using Experience-driven Procedural Content Generation via Reinforcement Learning (EDPCGRL)Code0
A Model Selection Approach for Corruption Robust Reinforcement Learning—0
Explaining Deep Reinforcement Learning Agents In The Atari Domain through a Surrogate Model—0
Near-Optimal Reward-Free Exploration for Linear Mixture MDPs with Plug-in Solver—0
Robotic Lever Manipulation using Hindsight Experience Replay and Shapley Additive Explanations—0
The Benefits of Being Categorical Distributional: Uncertainty-aware Regularized Exploration in Reinforcement Learning—0
Reinforcement Learning in Reward-Mixing MDPs—0
Augmenting Reinforcement Learning with Behavior Primitives for Diverse Manipulation TasksCode1
Bad-Policy Density: A Measure of Reinforcement Learning Hardness—0
How to Sense the World: Leveraging Hierarchy in Multimodal Perception for Robust Reinforcement Learning AgentsCode0
Generalization in Deep RL for TSP Problems via Equivariance and Local Search—0
Learning Pessimism for Robust and Efficient Off-Policy Reinforcement Learning—0
Learning Multi-Objective Curricula for Robotic Policy LearningCode0
Optimized Recommender Systems with Deep Reinforcement LearningCode0
Compositional Q-learning for electrolyte repletion with imbalanced patient sub-populations—0
The Information Geometry of Unsupervised Reinforcement LearningCode1
Pretraining & Reinforcement Learning: Sharpening the Axe Before Cutting the Tree—0
Mismatched No More: Joint Model-Policy Optimization for Model-Based RLCode1
Replay-Guided Adversarial Environment DesignCode1
Multi-Agent Constrained Policy OptimisationCode1
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Benchmark Results

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