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 6376–6400 of 15113 papers

TitleStatusHype
Tackling Variabilities in Autonomous Driving—0
Tackling Visual Control via Multi-View Exploration Maximization—0
TACO-RL: Task Aware Prompt Compression Optimization with Reinforcement Learning—0
TACT: A Transfer Actor-Critic Learning Framework for Energy Saving in Cellular Radio Access Networks—0
Tactical Reward Shaping: Bypassing Reinforcement Learning with Strategy-Based Goals—0
Tactics of Adversarial Attack on Deep Reinforcement Learning Agents—0
TacticZero: Learning to Prove Theorems from Scratch with Deep Reinforcement Learning—0
Tactile Active Inference Reinforcement Learning for Efficient Robotic Manipulation Skill Acquisition—0
Taming an autonomous surface vehicle for path following and collision avoidance using deep reinforcement learning—0
Taming Communication and Sample Complexities in Decentralized Policy Evaluation for Cooperative Multi-Agent Reinforcement Learning—0
Taming Continuous Posteriors for Latent Variational Dialogue Policies—0
Taming "data-hungry" reinforcement learning? Stability in continuous state-action spaces—0
Taming Lagrangian Chaos with Multi-Objective Reinforcement Learning—0
Taming OOD Actions for Offline Reinforcement Learning: An Advantage-Based Approach—0
Tangent Space Least Adaptive Clustering—0
TAP-Net: Transport-and-Pack using Reinforcement Learning—0
CopyCAT: Taking Control of Neural Policies with Constant Attacks—0
Targeted Data Acquisition for Evolving Negotiation Agents—0
Targeted Environment Design from Offline Data—0
Target-independent XLA optimization using Reinforcement Learning—0
Target Network and Truncation Overcome The Deadly Triad in Q-Learning—0
Targets in Reinforcement Learning to solve Stackelberg Security Games—0
Target Transfer Q-Learning and Its Convergence Analysis—0
TarGF: Learning Target Gradient Field to Rearrange Objects without Explicit Goal Specification—0
TAR: Teacher-Aligned Representations via Contrastive Learning for Quadrupedal Locomotion—0
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Benchmark Results

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