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 5351–5375 of 15113 papers

TitleStatusHype
Improving width-based planning with compact policies—0
Improving Zero-shot Generalization in Offline Reinforcement Learning using Generalized Similarity Functions—0
IM-RAG: Multi-Round Retrieval-Augmented Generation Through Learning Inner Monologues—0
IMRL: Integrating Visual, Physical, Temporal, and Geometric Representations for Enhanced Food Acquisition—0
I'm sorry Dave, I'm afraid I can't do that, Deep Q-learning from forbidden action—0
Inapplicable Actions Learning for Knowledge Transfer in Reinforcement Learning—0
Incentive-based demand response for smart grid with reinforcement learning and deep neural network—0
Incentivizing an Unknown Crowd—0
Generalizing Emergent Communication—0
In-context Exploration-Exploitation for Reinforcement Learning—0
Large Language Models can Implement Policy Iteration—0
Incorporating Consistency Verification into Neural Data-to-Document Generation—0
Incorporating Deception into CyberBattleSim for Autonomous Defense—0
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
Show:102550
← PrevPage 215 of 605Next →

Benchmark Results

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