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 11651–11675 of 15113 papers

TitleStatusHype
Incorporating Explicit Uncertainty Estimates into Deep Offline Reinforcement Learning—0
Incorporating Graph Attention Mechanism into Knowledge Graph Reasoning Based on Deep Reinforcement Learning—0
Incorporating Human Domain Knowledge into Large Scale Cost Function Learning—0
Incorporating Pragmatic Reasoning Communication into Emergent Language—0
Incorporating Relational Background Knowledge into Reinforcement Learning via Differentiable Inductive Logic Programming—0
Incorporating Rivalry in Reinforcement Learning for a Competitive Game—0
Incorporating Stylistic Lexical Preferences in Generative Language Models—0
Incorporating Voice Instructions in Model-Based Reinforcement Learning for Self-Driving Cars—0
Incorporation of Deep Neural Network & Reinforcement Learning with Domain Knowledge—0
Increasing Energy Efficiency of Massive-MIMO Network via Base Stations Switching using Reinforcement Learning and Radio Environment Maps—0
Increasing the Efficiency of Policy Learning for Autonomous Vehicles by Multi-Task Representation Learning—0
Data Informed Residual Reinforcement Learning for High-Dimensional Robotic Tracking Control—0
Incremental Hierarchical Reinforcement Learning with Multitask LMDPs—0
Incrementality Bidding via Reinforcement Learning under Mixed and Delayed Rewards—0
Incrementally Learning Functions of the Return—0
Incremental Policy Gradients for Online Reinforcement Learning Control—0
Incremental Reinforcement Learning --- a New Continuous Reinforcement Learning Frame Based on Stochastic Differential Equation methods—0
Incremental Text to Speech for Neural Sequence-to-Sequence Models using Reinforcement Learning—0
Independent Learning in Stochastic Games—0
Independent Policy Gradient for Large-Scale Markov Potential Games: Sharper Rates, Function Approximation, and Game-Agnostic Convergence—0
Independent Policy Gradient Methods for Competitive Reinforcement Learning—0
Independent RL for Cooperative-Competitive Agents: A Mean-Field Perspective—0
Index Selection for NoSQL Database with Deep Reinforcement Learning—0
Individual-Level Inverse Reinforcement Learning for Mean Field Games—0
Individual specialization in multi-task environments with multiagent reinforcement learners—0
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Benchmark Results

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