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 1061–1070 of 15113 papers

TitleStatusHype
Robust Task Representations for Offline Meta-Reinforcement Learning via Contrastive LearningCode1
Deep Reinforcement Learning for Turbulence Modeling in Large Eddy SimulationsCode1
Robust Deep Reinforcement Learning through Bootstrapped Opportunistic CurriculumCode1
EAGER: Asking and Answering Questions for Automatic Reward Shaping in Language-guided RLCode1
MASER: Multi-Agent Reinforcement Learning with Subgoals Generated from Experience Replay BufferCode1
Benchmarking Constraint Inference in Inverse Reinforcement LearningCode1
DNA: Proximal Policy Optimization with a Dual Network ArchitectureCode1
Sampling Efficient Deep Reinforcement Learning through Preference-Guided Stochastic ExplorationCode1
A deep inverse reinforcement learning approach to route choice modeling with context-dependent rewardsCode1
Fast Population-Based Reinforcement Learning on a Single MachineCode1
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Benchmark Results

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