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 5251–5275 of 15113 papers

TitleStatusHype
Rethinking Decision Transformer via Hierarchical Reinforcement Learning—0
A Micro-Objective Perspective of Reinforcement Learning—0
Decoupled Exploration and Exploitation Policies for Sample-Efficient Reinforcement Learning—0
Rethinking Exposure Bias In Language Modeling—0
Rethinking Modern Communication from Semantic Coding to Semantic Communication—0
Rethinking Pareto Approaches in Constrained Reinforcement Learning—0
Rethinking Population-assisted Off-policy Reinforcement Learning—0
Rethinking Pruning for Backdoor Mitigation: An Optimization Perspective—0
Rethinking Reinforcement Learning based Logic Synthesis—0
Rethinking Reinforcement Learning for Recommendation: A Prompt Perspective—0
Rethinking Robustness Assessment: Adversarial Attacks on Learning-based Quadrupedal Locomotion Controllers—0
Rethinking State Disentanglement in Causal Reinforcement Learning—0
Rethinking the Discount Factor in Reinforcement Learning: A Decision Theoretic Approach—0
ReTool: Reinforcement Learning for Strategic Tool Use in LLMs—0
Retrieval-Augmented Reinforcement Learning—0
Retrieval of surgical phase transitions using reinforcement learning—0
Return Augmented Decision Transformer for Off-Dynamics Reinforcement Learning—0
Return-Based Contrastive Representation Learning for Reinforcement Learning—0
Return-based Scaling: Yet Another Normalisation Trick for Deep RL—0
Return Dispersion as an Estimator of Learning Potential for Prioritized Level Replay—0
Revealing Covert Attention by Analyzing Human and Reinforcement Learning Agent Gameplay—0
Revealing higher-order neural representations of uncertainty with the Noise Estimation through Reinforcement-based Diffusion (NERD) model—0
Revealing the learning process in reinforcement learning agents through attention-oriented metrics—0
ReVeal: Self-Evolving Code Agents via Iterative Generation-Verification—0
Reverse Curriculum Generation for Reinforcement Learning—0
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Benchmark Results

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