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 8076–8100 of 15113 papers

TitleStatusHype
Optimizing the Long-Term Behaviour of Deep Reinforcement Learning for Pushing and Grasping—0
Optimizing Traffic Lights with Multi-agent Deep Reinforcement Learning and V2X communication—0
Optimizing Trajectories for Highway Driving with Offline Reinforcement Learning—0
Optimizing Wireless Discontinuous Reception via MAC Signaling Learning—0
Option Compatible Reward Inverse Reinforcement Learning—0
Option Discovery in Hierarchical Reinforcement Learning using Spatio-Temporal Clustering—0
Option Discovery Using LLM-guided Semantic Hierarchical Reinforcement Learning—0
Option Encoder: A Framework for Discovering a Policy Basis in Reinforcement Learning—0
Option Hedging with Risk Averse Reinforcement Learning—0
Options as responses: Grounding behavioural hierarchies in multi-agent RL—0
OPtions as REsponses: Grounding behavioural hierarchies in multi-agent reinforcement learning—0
Options Discovery with Budgeted Reinforcement Learning—0
OptLayer - Practical Constrained Optimization for Deep Reinforcement Learning in the Real World—0
Oracle-Efficient Reinforcement Learning for Max Value Ensembles—0
Oracle-free Reinforcement Learning in Mean-Field Games along a Single Sample Path—0
Oracle Inequalities for Model Selection in Offline Reinforcement Learning—0
Oracles & Followers: Stackelberg Equilibria in Deep Multi-Agent Reinforcement Learning—0
OrbitZoo: Multi-Agent Reinforcement Learning Environment for Orbital Dynamics—0
Ordering-Based Causal Discovery with Reinforcement Learning—0
Order-Optimal Instance-Dependent Bounds for Offline Reinforcement Learning with Preference Feedback—0
Organ localisation using supervised and semi supervised approaches combining reinforcement learning with imitation learning—0
Orthogonal Estimation of Wasserstein Distances—0
Orthogonal Policy Gradient and Autonomous Driving Application—0
OSS Mentor A framework for improving developers contributions via deep reinforcement learning—0
OTC: Optimal Tool Calls via Reinforcement Learning—0
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Benchmark Results

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