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 5776–5800 of 15113 papers

TitleStatusHype
LASER: Learning a Latent Action Space for Efficient Reinforcement Learning—0
Latent-Conditioned Policy Gradient for Multi-Objective Deep Reinforcement Learning—0
Latent forward model for Real-time Strategy game planning with incomplete information—0
LatentPoison -- Adversarial Attacks On The Latent Space—0
Latent Properties of Lifelong Learning Systems—0
Latent Space Policies for Hierarchical Reinforcement Learning—0
Latent Space Reinforcement Learning for Steering Angle Prediction—0
Latent Variable Representation for Reinforcement Learning—0
Launchpad: Learning to Schedule Using Offline and Online RL Methods—0
LAVA: Latent Action Spaces via Variational Auto-encoding for Dialogue Policy Optimization—0
Laxity-Aware Scalable Reinforcement Learning for HVAC Control—0
Lazy-MDPs: Towards Interpretable Reinforcement Learning by Learning When to Act—0
LBGP: Learning Based Goal Planning for Autonomous Following in Front—0
LDSA: Learning Dynamic Subtask Assignment in Cooperative Multi-Agent Reinforcement Learning—0
Leader Reward for POMO-Based Neural Combinatorial Optimization—0
Multitask Neuroevolution for Reinforcement Learning with Long and Short Episodes—0
Learn 2 Rage: Experiencing The Emotional Roller Coaster That Is Reinforcement Learning—0
Learnable Behavior Control: Breaking Atari Human World Records via Sample-Efficient Behavior Selection—0
Learnable Triangulation for Deep Learning-based 3D Reconstruction of Objects of Arbitrary Topology from Single RGB Images—0
Learn A Flexible Exploration Model for Parameterized Action Markov Decision Processes—0
LearnAlign: Reasoning Data Selection for Reinforcement Learning in Large Language Models Based on Improved Gradient Alignment—0
Learn Continuously, Act Discretely: Hybrid Action-Space Reinforcement Learning For Optimal Execution—0
Learned Controllers for Agile Quadrotors in Pursuit-Evasion Games—0
Learned Graph Rewriting with Equality Saturation: A New Paradigm in Relational Query Rewrite and Beyond—0
Learn Fine-grained Adaptive Loss for Multiple Anatomical Landmark Detection in Medical Images—0
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Benchmark Results

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