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 2301–2325 of 15113 papers

TitleStatusHype
BeamDojo: Learning Agile Humanoid Locomotion on Sparse Footholds—0
Analysis of Randomization Effects on Sim2Real Transfer in Reinforcement Learning for Robotic Manipulation Tasks—0
BCRLSP: An Offline Reinforcement Learning Framework for Sequential Targeted Promotion—0
Analysis of Information Propagation in Ethereum Network Using Combined Graph Attention Network and Reinforcement Learning to Optimize Network Efficiency and Scalability—0
Adaptive Trade-Offs in Off-Policy Learning—0
Taming Reachability Analysis of DNN-Controlled Systems via Abstraction-Based Training—0
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems—0
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems—0
Analysis of Evolutionary Behavior in Self-Learning Media Search Engines—0
Adaptive Torque Control of Exoskeletons under Spasticity Conditions via Reinforcement Learning—0
CoNES: Convex Natural Evolutionary Strategies—0
Configuration Path Control—0
Bayesian Transfer Reinforcement Learning with Prior Knowledge Rules—0
Bayesian Sequential Optimal Experimental Design for Nonlinear Models Using Policy Gradient Reinforcement Learning—0
Analysis of Agent Expertise in Ms. Pac-Man using Value-of-Information-based Policies—0
Analysis and Improvement of Policy Gradient Estimation—0
Bayesian Residual Policy Optimization: Scalable Bayesian Reinforcement Learning with Clairvoyant Experts—0
Adaptive Temporal Difference Learning with Linear Function Approximation—0
Conditional Kernel Imitation Learning for Continuous State Environments—0
Bayesian Reinforcement Learning in Factored POMDPs—0
Bayesian Reinforcement Learning: A Survey—0
A Comparison of Self-Play Algorithms Under a Generalized Framework—0
Bayesian Q-learning With Imperfect Expert Demonstrations—0
Bayesian policy selection using active inference—0
Analysing Deep Reinforcement Learning Agents Trained with Domain Randomisation—0
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Benchmark Results

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