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 7951–7975 of 15113 papers

TitleStatusHype
Open Problem: Approximate Planning of POMDPs in the class of Memoryless Policies—0
Open Problem: Order Optimal Regret Bounds for Kernel-Based Reinforcement Learning—0
Open Problems and Modern Solutions for Deep Reinforcement Learning—0
Open Problem: Tight Online Confidence Intervals for RKHS Elements—0
Open Vision Reasoner: Transferring Linguistic Cognitive Behavior for Visual Reasoning—0
Operator Shifting for Model-based Policy Evaluation—0
Operator Deep Q-Learning: Zero-Shot Reward Transferring in Reinforcement Learning—0
Operator Splitting Value Iteration—0
Opinion shaping in social networks using reinforcement learning—0
Opportunistic Episodic Reinforcement Learning—0
Opportunities of Hybrid Model-based Reinforcement Learning for Cell Therapy Manufacturing Process Control—0
Opportunities of Reinforcement Learning in South Africa's Just Transition—0
Low-Rank MDPs with Continuous Action Spaces—0
Optimal and Learning Control for Autonomous Robots—0
Optimal Attacks on Reinforcement Learning Policies—0
Optimal Beam Association for High Mobility mmWave Vehicular Networks: Lightweight Parallel Reinforcement Learning Approach—0
Optimal Bidding Strategy without Exploration in Real-time Bidding—0
Optimal Charging Method for Effective Li-ion Battery Life Extension Based on Reinforcement Learning—0
Optimal Conservative Offline RL with General Function Approximation via Augmented Lagrangian—0
Optimal control barrier functions for RL based safe powertrain control—0
Optimal Control of District Cooling Energy Plant with Reinforcement Learning and MPC—0
Optimal control of eye-movements during visual search—0
Optimal Control of Material Micro-Structures—0
Optimal control of point-to-point navigation in turbulent time-dependent flows using Reinforcement Learning—0
Optimal coordination of resources: A solution from reinforcement learning—0
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Benchmark Results

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