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 8701–8725 of 15113 papers

TitleStatusHype
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
“Other-Play” for Zero-Shot Coordination—0
OThink-MR1: Stimulating multimodal generalized reasoning capabilities via dynamic reinforcement learning—0
OTTR: Off-Road Trajectory Tracking using Reinforcement Learning—0
Outcome-Constrained Large Language Models for Countering Hate Speech—0
Outcome-Driven Reinforcement Learning via Variational Inference—0
Outcome-Guided Counterfactuals for Reinforcement Learning Agents from a Jointly Trained Generative Latent Space—0
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Benchmark Results

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