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 8251–8275 of 15113 papers

TitleStatusHype
Obstacle Avoidance for UAS in Continuous Action Space Using Deep Reinforcement Learning—0
Obtain Employee Turnover Rate and Optimal Reduction Strategy Based On Neural Network and Reinforcement Learning—0
OCALM: Object-Centric Assessment with Language Models—0
Occam's razor is insufficient to infer the preferences of irrational agents—0
Occupancy Information Ratio: Infinite-Horizon, Information-Directed, Parameterized Policy Search—0
ODE-based Recurrent Model-free Reinforcement Learning for POMDPs—0
ODGR: Online Dynamic Goal Recognition—0
Off-Beat Multi-Agent Reinforcement Learning—0
Off-dynamics Conditional Diffusion Planners—0
Off-Dynamics Inverse Reinforcement Learning from Hetero-Domain—0
Offline and Distributional Reinforcement Learning for Radio Resource Management—0
Offline and Distributional Reinforcement Learning for Wireless Communications—0
Offline Constrained Multi-Objective Reinforcement Learning via Pessimistic Dual Value Iteration—0
Offline Decentralized Multi-Agent Reinforcement Learning—0
Offline Deep Reinforcement Learning for Dynamic Pricing of Consumer Credit—0
Forgetting and Imbalance in Robot Lifelong Learning with Off-policy Data—0
Offline Evaluation for Reinforcement Learning-based Recommendation: A Critical Issue and Some Alternatives—0
Offline Fictitious Self-Play for Competitive Games—0
Offline Guarded Safe Reinforcement Learning for Medical Treatment Optimization Strategies—0
Offline Hierarchical Reinforcement Learning via Inverse Optimization—0
Offline Imitation Learning from Multiple Baselines with Applications to Compiler Optimization—0
Offline Imitation Learning Through Graph Search and Retrieval—0
Offline Inverse Constrained Reinforcement Learning for Safe-Critical Decision Making in Healthcare—0
Offline Inverse Reinforcement Learning—0
Offline Learning in Markov Games with General Function Approximation—0
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Benchmark Results

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