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 8076–8100 of 15113 papers

TitleStatusHype
Offline Reinforcement Learning with Soft Behavior Regularization—0
Safe Autonomous Racing via Approximate Reachability on Ego-vision—0
HAVEN: Hierarchical Cooperative Multi-Agent Reinforcement Learning with Dual Coordination Mechanism—0
Extending Environments To Measure Self-Reflection In Reinforcement LearningCode0
Block Contextual MDPs for Continual Learning—0
Improving the sample-efficiency of neural architecture search with reinforcement learningCode0
Feudal Reinforcement Learning by Reading Manuals—0
NeurIPS 2021 Competition IGLU: Interactive Grounded Language Understanding in a Collaborative Environment—0
Next-Best-View Estimation based on Deep Reinforcement Learning for Active Object ClassificationCode0
Reinforcement Learning for Standards Design—0
On Covariate Shift of Latent Confounders in Imitation and Reinforcement Learning—0
Reward-Free Model-Based Reinforcement Learning with Linear Function Approximation—0
Power and Accountability in RL-driven Environmental Policy—0
Provably Efficient Reinforcement Learning in Decentralized General-Sum Markov Games—0
Temporal Abstraction in Reinforcement Learning with the Successor Representation—0
Learning Efficient Multi-Agent Cooperative Visual Exploration—0
DQN-based Beamforming for Uplink mmWave Cellular-Connected UAVs—0
GridLearn: Multiagent Reinforcement Learning for Grid-Aware Building Energy Management—0
Deciding What's Fair: Challenges of Applying Reinforcement Learning in Online Marketplaces—0
On Improving Model-Free Algorithms for Decentralized Multi-Agent Reinforcement Learning—0
Learning a subspace of policies for online adaptation in Reinforcement Learning—0
An Automated Portfolio Trading System with Feature Preprocessing and Recurrent Reinforcement Learning—0
Learning from Ambiguous Demonstrations with Self-Explanation Guided Reinforcement LearningCode0
Learning Temporally-Consistent Representations for Data-Efficient Reinforcement LearningCode0
Bid Optimization using Maximum Entropy Reinforcement Learning—0
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Benchmark Results

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