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 10601–10625 of 15113 papers

TitleStatusHype
Synthetic Health-related Longitudinal Data with Mixed-type Variables Generated using Diffusion Models—0
Synthetic Sample Selection via Reinforcement Learning—0
SySLLM: Generating Synthesized Policy Summaries for Reinforcement Learning Agents Using Large Language Models—0
Systematic Generalisation through Task Temporal Logic and Deep Reinforcement Learning—0
System Design for an Integrated Lifelong Reinforcement Learning Agent for Real-Time Strategy Games—0
Systems Theoretic Process Analysis of a Run Time Assured Neural Network Control System—0
Tabular and Deep Reinforcement Learning for Gittins Index—0
Tackling Heavy-Tailed Rewards in Reinforcement Learning with Function Approximation: Minimax Optimal and Instance-Dependent Regret Bounds—0
Model-based Offline Reinforcement Learning with Lower Expectile Q-Learning—0
Tackling Morpion Solitaire with AlphaZero-likeRanked Reward Reinforcement Learning—0
Tackling Real-World Autonomous Driving using Deep Reinforcement Learning—0
Tackling the Zero-Shot Reinforcement Learning Loss Directly—0
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
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Benchmark Results

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