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 10351–10400 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
Contextual Bandits for adapting to changing User preferences over time—0
Learning a Contact-Adaptive Controller for Robust, Efficient Legged Locomotion—0
DISPATCH: Design Space Exploration of Cyber-Physical Systems—0
Learn to Exceed: Stereo Inverse Reinforcement Learning with Concurrent Policy Optimization—0
Regret Bounds and Reinforcement Learning Exploration of EXP-based Algorithms—0
Lyapunov-Based Reinforcement Learning for Decentralized Multi-Agent Control—0
Multiplayer Support for the Arcade Learning Environment—0
Construction of Polar Codes with Reinforcement Learning—0
A Contraction Approach to Model-based Reinforcement Learning—0
GRAC: Self-Guided and Self-Regularized Actor-CriticCode0
HTMRL: Biologically Plausible Reinforcement Learning with Hierarchical Temporal MemoryCode0
Deep Reinforcement Learning for Closed-Loop Blood Glucose Control—0
Private Reinforcement Learning with PAC and Regret Guarantees—0
Efficient Reinforcement Learning Development with RLzoo—0
Reinforcement Learning for Weakly Supervised Temporal Grounding of Natural Language in Untrimmed Videos—0
Reconstructing Actions To Explain Deep Reinforcement Learning—0
Reward Maximisation through Discrete Active Inference—0
Knowledge-Assisted Deep Reinforcement Learning in 5G Scheduler Design: From Theoretical Framework to Implementation—0
GeneraLight: Improving Environment Generalization of Traffic Signal Control via Meta Reinforcement Learning—0
DRL-FAS: A Novel Framework Based on Deep Reinforcement Learning for Face Anti-Spoofing—0
Theory of Mind with Guilt Aversion Facilitates Cooperative Reinforcement Learning—0
Time your hedge with Deep Reinforcement Learning—0
Transfer Learning in Deep Reinforcement Learning: A Survey—0
Reinforcement Learning for Strategic Recommendations—0
Soft policy optimization using dual-track advantage estimator—0
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Benchmark Results

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