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 4431–4440 of 15113 papers

TitleStatusHype
lilGym: Natural Language Visual Reasoning with Reinforcement Learning—0
Leveraging Fully Observable Policies for Learning under Partial ObservabilityCode0
GEC: A Unified Framework for Interactive Decision Making in MDP, POMDP, and Beyond—0
Sensor Control for Information Gain in Dynamic, Sparse and Partially Observed Environments—0
Reinforcement Learning in Non-Markovian Environments—0
Oracle Inequalities for Model Selection in Offline Reinforcement Learning—0
Scalable Multi-Agent Reinforcement Learning through Intelligent Information AggregationCode1
Theta-Resonance: A Single-Step Reinforcement Learning Method for Design Space Exploration—0
Deep Reinforcement Learning for IRS Phase Shift Design in Spatiotemporally Correlated Environments—0
Causal Counterfactuals for Improving the Robustness of Reinforcement LearningCode1
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Benchmark Results

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