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

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Showing 6401–6425 of 15113 papers

TitleStatusHype
TASAC: a twin-actor reinforcement learning framework with stochastic policy for batch process control—0
Task-agnostic Exploration in Reinforcement Learning—0
Task-agnostic Pre-training and Task-guided Fine-tuning for Versatile Diffusion Planner—0
Task-Completion Dialogue Policy Learning via Monte Carlo Tree Search with Dueling Network—0
Task-driven Discovery of Perceptual Schemas for Generalization in Reinforcement Learning—0
A Dual Curriculum Learning Framework for Multi-UAV Pursuit-Evasion in Diverse Environments—0
Task-Guided Inverse Reinforcement Learning Under Partial Information—0
Task Independent Capsule-Based Agents for Deep Q-Learning—0
Task-Induced Representation Learning—0
Intelligent Offloading in Vehicular Edge Computing: A Comprehensive Review of Deep Reinforcement Learning Approaches and Architectures—0
Task-optimal data-driven surrogate models for eNMPC via differentiable simulation and optimization—0
Task-Oriented Communication Design at Scale—0
Task-oriented Design through Deep Reinforcement Learning—0
TauRieL: Targeting Traveling Salesman Problem with a deep reinforcement learning inspired architecture—0
Taxable Stock Trading with Deep Reinforcement Learning—0
Taylor Expansion of Discount Factors—0
Taylor Expansion Policy Optimization—0
TBQ(σ): Improving Efficiency of Trace Utilization for Off-Policy Reinforcement Learning—0
T-Cell Receptor Optimization with Reinforcement Learning and Mutation Policies for Precesion Immunotherapy—0
Teacher-Critical Training Strategies for Image Captioning—0
Teacher-student curriculum learning for reinforcement learning—0
Teaching a Robot to Walk Using Reinforcement Learning—0
Teaching GANs to Sketch in Vector Format—0
Teaching LLMs for Step-Level Automatic Math Correction via Reinforcement Learning—0
Teaching robots to perceive time -- A reinforcement learning approach (Extended version)—0
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Benchmark Results

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