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 14951–15000 of 15113 papers

TitleStatusHype
MONEYBaRL: Exploiting pitcher decision-making using Reinforcement Learning—0
Learning in games via reinforcement and regularization—0
Practical Kernel-Based Reinforcement Learning—0
Extreme State Aggregation Beyond MDPs—0
Reinforcement Learning Based Algorithm for the Maximization of EV Charging Station Revenue—0
Thompson Sampling for Learning Parameterized Markov Decision Processes—0
Reinforcement and Imitation Learning via Interactive No-Regret Learning—0
Deterministic Policy Gradient AlgorithmsCode0
Personalized Medical Treatments Using Novel Reinforcement Learning Algorithms—0
Multi-objective Reinforcement Learning with Continuous Pareto Frontier Approximation Supplementary Material—0
Model-based Reinforcement Learning and the Eluder Dimension—0
Single-Agent vs. Multi-Agent Techniques for Concurrent Reinforcement Learning of Negotiation Dialogue Policies—0
Comparing Multi-label Classification with Reinforcement Learning for Summarisation of Time-series Data—0
Proximal Reinforcement Learning: A New Theory of Sequential Decision Making in Primal-Dual Spaces—0
Projective simulation applied to the grid-world and the mountain-car problem—0
Off-Policy Shaping Ensembles in Reinforcement Learning—0
Structural Return Maximization for Reinforcement Learning—0
Selecting Near-Optimal Approximate State Representations in Reinforcement Learning—0
DINASTI: Dialogues with a Negotiating Appointment Setting Interface—0
Deep Learning in Neural Networks: An OverviewCode0
Optimizing the CVaR via SamplingCode1
Scalable Planning and Learning for Multiagent POMDPs: Extended VersionCode1
Undirected Machine Translation with Discriminative Reinforcement Learning—0
Comparison of Multi-agent and Single-agent Inverse Learning on a Simulated Soccer Example—0
Multi-agent Inverse Reinforcement Learning for Two-person Zero-sum Games—0
Simultaneous Perturbation Algorithms for Batch Off-Policy Search—0
Near-optimal Reinforcement Learning in Factored MDPs—0
Off-Policy General Value Functions to Represent Dynamic Role Assignments in RoboCup 3D Soccer SimulationCode1
Intrinsically Motivated Learning of Visual Motion Perception and Smooth Pursuit—0
Better Optimism By Bayes: Adaptive Planning with Rich Models—0
Safe Exploration of State and Action Spaces in Reinforcement Learning—0
Generalization and Exploration via Randomized Value FunctionsCode0
Kalman Temporal Differences—0
Non-Deterministic Policies in Markovian Decision Processes—0
A Multiagent Reinforcement Learning Algorithm with Non-linear Dynamics—0
Learning Partially Observable Deterministic Action Models—0
Exploiting generalisation symmetries in accuracy-based learning classifier systems: An initial study—0
DJ-MC: A Reinforcement-Learning Agent for Music Playlist Recommendation—0
Optimal Demand Response Using Device Based Reinforcement Learning—0
Playing Atari with Deep Reinforcement LearningCode1
Using reinforcement learning to find an optimal set of featuresCode0
Projected Natural Actor-Critic—0
Reinforcement Learning in Robust Markov Decision Processes—0
Policy Shaping: Integrating Human Feedback with Reinforcement Learning—0
Bellman Error Based Feature Generation using Random Projections on Sparse Spaces—0
Efficient Exploration and Value Function Generalization in Deterministic Systems—0
Off-policy reinforcement learning for H_ control design—0
Risk-sensitive Reinforcement Learning—0
Exploration in Interactive Personalized Music Recommendation: A Reinforcement Learning Approach—0
Reinforcement Learning for Matrix Computations: PageRank as an Example—0
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Benchmark Results

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