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

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Showing 4901–4925 of 15113 papers

TitleStatusHype
Reward Shaping via Diffusion Process in Reinforcement Learning—0
Adaptive Ordered Information Extraction with Deep Reinforcement LearningCode0
On the Model-Misspecification in Reinforcement Learning—0
AdaStop: adaptive statistical testing for sound comparisons of Deep RL agentsCode0
Enhancing variational quantum state diagonalization using reinforcement learning techniquesCode0
Acceleration in Policy Optimization—0
The RL Perceptron: Generalisation Dynamics of Policy Learning in High Dimensions—0
Genes in Intelligent AgentsCode0
Active Policy Improvement from Multiple Black-box OraclesCode0
Do as I can, not as I get—0
Bootstrapped Representations in Reinforcement Learning—0
Temporal Difference Learning with Experience Replay—0
Semi-Offline Reinforcement Learning for Optimized Text GenerationCode0
The False Dawn: Reevaluating Google's Reinforcement Learning for Chip Macro Placement—0
Real-Time Network-Level Traffic Signal Control: An Explicit Multiagent Coordination Method—0
Offline Multi-Agent Reinforcement Learning with Coupled Value Factorization—0
Predictive Maneuver Planning with Deep Reinforcement Learning (PMP-DRL) for comfortable and safe autonomous driving—0
Low-Switching Policy Gradient with Exploration via Online Sensitivity Sampling—0
Langevin Thompson Sampling with Logarithmic Communication: Bandits and Reinforcement Learning—0
Granger Causal Interaction Skill Chains—0
A reinforcement learning strategy for p-adaptation in high order solvers—0
Provably Efficient Offline Reinforcement Learning with Perturbed Data Sources—0
Off-policy Evaluation in Doubly Inhomogeneous EnvironmentsCode0
Skill-Critic: Refining Learned Skills for Hierarchical Reinforcement Learning—0
Simple Embodied Language Learning as a Byproduct of Meta-Reinforcement Learning—0
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Benchmark Results

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