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 6851–6875 of 15113 papers

TitleStatusHype
Transfer of Reinforcement Learning-Based Controllers from Model- to Hardware-in-the-Loop—0
Transfer of Temporal Logic Formulas in Reinforcement Learning—0
Transfer of Value Functions via Variational Methods—0
Transferred Energy Management Strategies for Hybrid Electric Vehicles Based on Driving Conditions Recognition—0
Transferred Q-learning—0
Transfer Reinforcement Learning under Unobserved Contextual Information—0
Transferring Agent Behaviors from Videos via Motion GANs—0
Transferring Autonomous Driving Knowledge on Simulated and Real Intersections—0
Transferring Deep Reinforcement Learning with Adversarial Objective and Augmentation—0
Transferring Domain Knowledge with an Adviser in Continuous Tasks—0
Transferring Expectations in Model-based Reinforcement Learning—0
Transferring Knowledge for Reinforcement Learning in Contact-Rich Manipulation—0
Transferring Multiple Policies to Hotstart Reinforcement Learning in an Air Compressor Management Problem—0
Transferring Reinforcement Learning for DC-DC Buck Converter Control via Duty Ratio Mapping: From Simulation to Implementation—0
Transfer RL across Observation Feature Spaces via Model-Based Regularization—0
Transfer Value or Policy? A Value-centric Framework Towards Transferrable Continuous Reinforcement Learning—0
Transfer with Model Features in Reinforcement Learning—0
Transformation Coding: Simple Objectives for Equivariant Representations—0
Transformer-Based Fault-Tolerant Control for Fixed-Wing UAVs Using Knowledge Distillation and In-Context Adaptation—0
Transformer Based Reinforcement Learning For Games—0
Transformer Network-based Reinforcement Learning Method for Power Distribution Network (PDN) Optimization of High Bandwidth Memory (HBM)—0
Transformers are Meta-Reinforcement Learners—0
Transformers as Game Players: Provable In-context Game-playing Capabilities of Pre-trained Models—0
Transformers in Reinforcement Learning: A Survey—0
Transforming Cooling Optimization for Green Data Center via Deep Reinforcement Learning—0
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Benchmark Results

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