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 6551–6575 of 15113 papers

TitleStatusHype
The QLBS Q-Learner Goes NuQLear: Fitted Q Iteration, Inverse RL, and Option Portfolios—0
The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning—0
The Real Deal: A Review of Challenges and Opportunities in Moving Reinforcement Learning-Based Traffic Signal Control Systems Towards Reality—0
The Recurrent Reinforcement Learning Crypto Agent—0
There is no Accuracy-Interpretability Tradeoff in Reinforcement Learning for Mazes—0
There Is No Turning Back: A Self-Supervised Approach for Reversibility-Aware Reinforcement Learning—0
Reward Maximisation through Discrete Active Inference—0
The Remarkable Effectiveness of Combining Policy and Value Networks in A*-based Deep RL for AI Planning—0
The RL/LLM Taxonomy Tree: Reviewing Synergies Between Reinforcement Learning and Large Language Models—0
The RL Perceptron: Generalisation Dynamics of Policy Learning in High Dimensions—0
A Reinforcement Learning Based R-Tree for Spatial Data Indexing in Dynamic Environments—0
Thermal Control of Laser Powder Bed Fusion Using Deep Reinforcement Learning—0
The Role of Coverage in Online Reinforcement Learning—0
The Role of Diverse Replay for Generalisation in Reinforcement Learning—0
The Role of Environment Access in Agnostic Reinforcement Learning—0
The Role of Exploration for Task Transfer in Reinforcement Learning—0
The Role of Time Delay in Sim2real Transfer of Reinforcement Learning for Cyber-Physical Systems—0
The Sample-Complexity of General Reinforcement Learning—0
The Skill-Action Architecture: Learning Abstract Action Embeddings for Reinforcement Learning—0
The State-Action-Reward-State-Action Algorithm in Spatial Prisoner's Dilemma Game—0
The Statistical Complexity of Interactive Decision Making—0
The Steganographic Potentials of Language Models—0
The Surprising Ineffectiveness of Pre-Trained Visual Representations for Model-Based Reinforcement Learning—0
The Synergy Dilemma of Long-CoT SFT and RL: Investigating Post-Training Techniques for Reasoning VLMs—0
Theta-Resonance: A Single-Step Reinforcement Learning Method for Design Space Exploration—0
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Benchmark Results

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