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
An agent-driven semantical identifier using radial basis neural networks and reinforcement learning—0
RESEARCH ARTICLE A Reinforcement Learning Model of Joy, Distress, Hope and Fear—0
Inverse Reinforcement Learning with Multi-Relational Chains for Robot-Centered Smart Home—0
Probabilistic inverse reinforcement learning in unknown environments—0
Learning to Cooperate via Policy Search—0
Reinforcement Learning of Cooperative Persuasive Dialogue Policies using Framing—0
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
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
Intrinsically Motivated Learning of Visual Motion Perception and Smooth Pursuit—0
Better Optimism By Bayes: Adaptive Planning with Rich Models—0
Generalization and Exploration via Randomized Value FunctionsCode0
Safe Exploration of State and Action Spaces in Reinforcement Learning—0
Non-Deterministic Policies in Markovian Decision Processes—0
Kalman Temporal Differences—0
Learning Partially Observable Deterministic Action Models—0
A Multiagent Reinforcement Learning Algorithm with Non-linear Dynamics—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
Policy Shaping: Integrating Human Feedback with Reinforcement Learning—0
Projected Natural Actor-Critic—0
Reinforcement Learning in Robust Markov Decision Processes—0
Using reinforcement learning to find an optimal set of featuresCode0
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
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Benchmark Results

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