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 3051–3075 of 15113 papers

TitleStatusHype
Human-Level Control without Server-Grade HardwareCode0
Count-Based Exploration with the Successor RepresentationCode0
Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learningCode0
Counterexample Guided RL Policy Refinement Using Bayesian OptimizationCode0
Counterfactual-Augmented Importance Sampling for Semi-Offline Policy EvaluationCode0
Hybrid Reinforcement Learning with Expert State SequencesCode0
Constructing Non-Markovian Decision Process via History AggregatorCode0
Human-Inspired Framework to Accelerate Reinforcement LearningCode0
Reward Shaping for Human Learning via Inverse Reinforcement LearningCode0
Multi-Horizon Representations with Hierarchical Forward Models for Reinforcement LearningCode0
Counterfactual Explanation with Multi-Agent Reinforcement Learning for Drug Target PredictionCode0
A Structured Prediction Approach for Generalization in Cooperative Multi-Agent Reinforcement LearningCode0
Human-guided Robot Behavior Learning: A GAN-assisted Preference-based Reinforcement Learning ApproachCode0
Human level control through deep reinforcement learningCode0
Counterfactual State Explanations for Reinforcement Learning Agents via Generative Deep LearningCode0
ARES: Alternating Reinforcement Learning and Supervised Fine-Tuning for Enhanced Multi-Modal Chain-of-Thought Reasoning Through Diverse AI FeedbackCode0
Countering Reward Over-optimization in LLM with Demonstration-Guided Reinforcement LearningCode0
Course Recommender Systems Need to Consider the Job MarketCode0
A Study of Plasticity Loss in On-Policy Deep Reinforcement LearningCode0
Constraint Sampling Reinforcement Learning: Incorporating Expertise For Faster LearningCode0
HTMRL: Biologically Plausible Reinforcement Learning with Hierarchical Temporal MemoryCode0
HRL4IN: Hierarchical Reinforcement Learning for Interactive Navigation with Mobile ManipulatorsCode0
Hybrid Reward Architecture for Reinforcement LearningCode0
How to Build User Simulators to Train RL-based Dialog SystemsCode0
How Private Is Your RL Policy? An Inverse RL Based Analysis FrameworkCode0
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Benchmark Results

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