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 3601–3625 of 15113 papers

TitleStatusHype
User Retention-oriented Recommendation with Decision TransformerCode1
Provably Efficient Model-Free Algorithms for Non-stationary CMDPs—0
Understanding the Synergies between Quality-Diversity and Deep Reinforcement Learning—0
Optimal foraging strategies can be learnedCode0
Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-TuningCode1
Evolving Populations of Diverse RL Agents with MAP-Elites—0
Exploiting Contextual Structure to Generate Useful Auxiliary Tasks—0
Task Aware Dreamer for Task Generalization in Reinforcement Learning—0
Power and Interference Control for VLC-Based UDN: A Reinforcement Learning Approach—0
A Framework for History-Aware Hyperparameter Optimisation in Reinforcement Learning—0
Variance-aware robust reinforcement learning with linear function approximation under heavy-tailed rewards—0
Conceptual Reinforcement Learning for Language-Conditioned Tasks—0
Computably Continuous Reinforcement-Learning Objectives are PAC-learnable—0
GOATS: Goal Sampling Adaptation for Scooping with Curriculum Reinforcement Learning—0
Real-time scheduling of renewable power systems through planning-based reinforcement learning—0
Beware of Instantaneous Dependence in Reinforcement Learning—0
Recent Advances of Deep Robotic Affordance Learning: A Reinforcement Learning Perspective—0
MCTS-GEB: Monte Carlo Tree Search is a Good E-graph BuilderCode0
Using Memory-Based Learning to Solve Tasks with State-Action Constraints—0
RACCER: Towards Reachable and Certain Counterfactual Explanations for Reinforcement LearningCode0
Deep Occupancy-Predictive Representations for Autonomous Driving—0
Learning Bipedal Walking for Humanoids with Current FeedbackCode3
A Multiplicative Value Function for Safe and Efficient Reinforcement LearningCode1
Evolutionary Reinforcement Learning: A Survey—0
Learning When to Treat Business Processes: Prescriptive Process Monitoring with Causal Inference and Reinforcement LearningCode0
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Benchmark Results

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