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 7301–7325 of 15113 papers

TitleStatusHype
Maximizing Ensemble Diversity in Deep Reinforcement Learning—0
Reinforcement Learning under a Multi-agent Predictive State Representation Model: Method and Theory—0
On the benefits of deep RL in accelerated MRI sampling—0
Multi-batch Reinforcement Learning via Sample Transfer and Imitation Learning—0
LPMARL: Linear Programming based Implicit Task Assigment for Hiearchical Multi-Agent Reinforcement Learning—0
Neural Combinatorial Optimization with Reinforcement Learning : Solving theVehicle Routing Problem with Time Windows—0
Text Generation with Efficient (Soft) Q-Learning—0
The Essential Elements of Offline RL via Supervised Learning—0
Selective Token Generation for Few-shot Language Modeling—0
Towards Unknown-aware Deep Q-Learning—0
Zero-Shot Reward Specification via Grounded Natural Language—0
Task-driven Discovery of Perceptual Schemas for Generalization in Reinforcement Learning—0
Provably Filtering Exogenous Distractors using Multistep Inverse Dynamics—0
Should I Run Offline Reinforcement Learning or Behavioral Cloning?—0
The Remarkable Effectiveness of Combining Policy and Value Networks in A*-based Deep RL for AI Planning—0
MARNET: Backdoor Attacks against Value-Decomposition Multi-Agent Reinforcement Learning—0
Why so pessimistic? Estimating uncertainties for offline RL through ensembles, and why their independence matters.—0
Targeted Environment Design from Offline Data—0
Superior Performance with Diversified Strategic Control in FPS Games Using General Reinforcement Learning—0
Reinforcement Learning with Predictive Consistent Representations—0
SAFER: Data-Efficient and Safe Reinforcement Learning Through Skill Acquisition—0
Programmatic Reinforcement Learning without Oracles—0
OVD-Explorer: A General Information-theoretic Exploration Approach for Reinforcement Learning—0
Reachability Traces for Curriculum Design in Reinforcement Learning—0
Offline Reinforcement Learning for Large Scale Language Action Spaces—0
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Benchmark Results

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