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 9826–9850 of 15113 papers

TitleStatusHype
Is Reinforcement Learning More Difficult Than Bandits? A Near-optimal Algorithm Escaping the Curse of Horizon—0
Agent Environment Cycle Games—0
Towards Heterogeneous Multi-Agent Reinforcement Learning with Graph Neural Networks—0
Scalable Deep Reinforcement Learning for Ride-Hailing—0
Scheduling and Power Control for Wireless Multicast Systems via Deep Reinforcement Learning—0
Machine Learning in Event-Triggered Control: Recent Advances and Open Issues—0
Virtual Experience to Real World Application: Sidewalk Obstacle Avoidance Using Reinforcement Learning for Visually Impaired—0
Lineage Evolution Reinforcement Learning—0
Graph neural induction of value iteration—0
Inverse Rational Control with Partially Observable Continuous Nonlinear Dynamics—0
Complementary Meta-Reinforcement Learning for Fault-Adaptive Control—0
Reinforcement Learning-based N-ary Cross-Sentence Relation Extraction—0
Neurosymbolic Reinforcement Learning with Formally Verified ExplorationCode1
Symbolic Relational Deep Reinforcement Learning based on Graph Neural Networks and Autoregressive Policy DecompositionCode1
Continual Model-Based Reinforcement Learning with HypernetworksCode1
Bootstrapped Q-learning with Context Relevant Observation Pruning to Generalize in Text-based GamesCode0
Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey—0
Motion Planning by Reinforcement Learning for an Unmanned Aerial Vehicle in Virtual Open Space with Static Obstacles—0
Deep Reinforcement Learning for Process SynthesisCode1
CertRL: Formalizing Convergence Proofs for Value and Policy Iteration in CoqCode1
A Multi-Agent Deep Reinforcement Learning Approach for a Distributed Energy Marketplace in Smart Grids—0
Demand Responsive Dynamic Pricing Framework for Prosumer Dominated Microgrids using Multiagent Reinforcement Learning—0
ReLeaSER: A Reinforcement Learning Strategy for Optimizing Utilization Of Ephemeral Cloud Resources—0
Probabilistic Machine Learning for Healthcare—0
What is the Reward for Handwriting? -- Handwriting Generation by Imitation Learning—0
Show:102550
← PrevPage 394 of 605Next →

Benchmark Results

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