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 5951–5975 of 15113 papers

TitleStatusHype
Understanding the Evolution of Linear Regions in Deep Reinforcement LearningCode0
OSS Mentor A framework for improving developers contributions via deep reinforcement learning—0
MEET: A Monte Carlo Exploration-Exploitation Trade-off for Buffer SamplingCode0
Reachability-Aware Laplacian Representation in Reinforcement Learning—0
Reinforcement Learning and Bandits for Speech and Language Processing: Tutorial, Review and Outlook—0
Opportunistic Episodic Reinforcement Learning—0
MetaEMS: A Meta Reinforcement Learning-based Control Framework for Building Energy Management System—0
Active Predictive Coding: A Unified Neural Framework for Learning Hierarchical World Models for Perception and Planning—0
Learning General World Models in a Handful of Reward-Free Deployments—0
LEAGUE: Guided Skill Learning and Abstraction for Long-Horizon Manipulation—0
A Cooperative Reinforcement Learning Environment for Detecting and Penalizing Betrayal—0
Climate Change Policy Exploration using Reinforcement Learning—0
Faster and more diverse de novo molecular optimization with double-loop reinforcement learning using augmented SMILES—0
Attitude Control of Highly Maneuverable Aircraft Using an Improved Q-learning—0
Probing Transfer in Deep Reinforcement Learning without Task Engineering—0
Epistemic Monte Carlo Tree Search—0
Towards Quantum-Enabled 6G Slicing—0
On the connection between Bregman divergence and value in regularized Markov decision processes—0
Rate-Splitting for Intelligent Reflecting Surface-Aided Multiuser VR StreamingCode0
Deep Reinforcement Learning for Stabilization of Large-scale Probabilistic Boolean Networks—0
Redefining Counterfactual Explanations for Reinforcement Learning: Overview, Challenges and Opportunities—0
Continual Vision-based Reinforcement Learning with Group Symmetries—0
Integrating Policy Summaries with Reward Decomposition for Explaining Reinforcement Learning Agents—0
Group Distributionally Robust Reinforcement Learning with Hierarchical Latent Variables—0
Biologically Plausible Variational Policy Gradient with Spiking Recurrent Winner-Take-All NetworksCode0
Show:102550
← PrevPage 239 of 605Next →

Benchmark Results

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