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 10351–10375 of 15113 papers

TitleStatusHype
Reinforcement Learning-based N-ary Cross-Sentence Relation Extraction—0
Lineage Evolution Reinforcement Learning—0
Complementary Meta-Reinforcement Learning for Fault-Adaptive Control—0
Graph neural induction of value iteration—0
Inverse Rational Control with Partially Observable Continuous Nonlinear Dynamics—0
Bootstrapped Q-learning with Context Relevant Observation Pruning to Generalize in Text-based GamesCode0
Motion Planning by Reinforcement Learning for an Unmanned Aerial Vehicle in Virtual Open Space with Static Obstacles—0
Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey—0
ReLeaSER: A Reinforcement Learning Strategy for Optimizing Utilization Of Ephemeral Cloud Resources—0
Probabilistic Machine Learning for Healthcare—0
Robust Reinforcement Learning-based Autonomous Driving Agent for Simulation and Real World—0
What is the Reward for Handwriting? -- Handwriting Generation by Imitation Learning—0
Demand Responsive Dynamic Pricing Framework for Prosumer Dominated Microgrids using Multiagent Reinforcement Learning—0
A Multi-Agent Deep Reinforcement Learning Approach for a Distributed Energy Marketplace in Smart Grids—0
Is Q-Learning Provably Efficient? An Extended Analysis—0
A Centralised Soft Actor Critic Deep Reinforcement Learning Approach to District Demand Side Management through CityLearnCode0
Distributed Structured Actor-Critic Reinforcement Learning for Universal Dialogue Management—0
Deep Reinforcement Learning for On-line Dialogue State Tracking—0
SUMBT+LaRL: Effective Multi-domain End-to-end Neural Task-oriented Dialog System—0
Reinforcement Learning Approaches in Social Robotics—0
Mobile Cellular-Connected UAVs: Reinforcement Learning for Sky Limits—0
Rethinking Supervised Learning and Reinforcement Learning in Task-Oriented Dialogue SystemsCode0
Deep Reinforcement Learning Methods for Structure-Guided Processing Path OptimizationCode0
Dynamic Horizon Value Estimation for Model-based Reinforcement Learning—0
Human Engagement Providing Evaluative and Informative Advice for Interactive Reinforcement Learning—0
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Benchmark Results

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