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 6276–6300 of 15113 papers

TitleStatusHype
Structure Learning in Human Sequential Decision-Making—0
Structure Learning in Motor Control:A Deep Reinforcement Learning Model—0
Student/Teacher Advising through Reward Augmentation—0
Student-Teacher Curriculum Learning via Reinforcement Learning: Predicting Hospital Inpatient Admission Location—0
Stylistic Dialogue Generation via Information-Guided Reinforcement Learning Strategy—0
Sub-domain Modelling for Dialogue Management with Hierarchical Reinforcement Learning—0
Subgoal-based Reward Shaping to Improve Efficiency in Reinforcement Learning—0
Subgoal Discovery Using a Free Energy Paradigm and State Aggregations—0
Sub-Goal Trees -- a Framework for Goal-Based Reinforcement Learning—0
Sub-Goal Trees -- a Framework for Goal-Directed Trajectory Prediction and Optimization—0
Detecting Small Query Graphs in A Large Graph via Neural Subgraph Search—0
Subjective Reinforcement Learning for Open Complex Environments—0
Sublinear Least-Squares Value Iteration via Locality Sensitive Hashing—0
Sublinear Regret for a Class of Continuous-Time Linear-Quadratic Reinforcement Learning Problems—0
Sublinear Regret for Learning POMDPs—0
Suboptimal and trait-like reinforcement learning strategies correlate with midbrain encoding of prediction errors—0
Sub-optimal Policy Aided Multi-Agent Reinforcement Learning for Flocking Control—0
Sub-policy Adaptation for Hierarchical Reinforcement Learning—0
Sub-policy Adaptation for Hierarchical Reinforcement Learning—0
Subtask-Aware Visual Reward Learning from Segmented Demonstrations—0
Sub-Task Discovery with Limited Supervision: A Constrained Clustering Approach—0
Successive Over Relaxation Q-Learning—0
Successor Feature Neural Episodic Control—0
Successor Features for Transfer in Reinforcement Learning—0
Successor Features Combine Elements of Model-Free and Model-based Reinforcement Learning—0
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Benchmark Results

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