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 3061–3070 of 15113 papers

TitleStatusHype
Reinforced Self-Training (ReST) for Language Modeling—0
Improving Sample Efficiency of Model-Free Algorithms for Zero-Sum Markov Games—0
ReProHRL: Towards Multi-Goal Navigation in the Real World using Hierarchical Agents—0
IMM: An Imitative Reinforcement Learning Approach with Predictive Representation Learning for Automatic Market Making—0
Partially Observable Multi-Agent Reinforcement Learning with Information Sharing—0
Planning to Learn: A Novel Algorithm for Active Learning during Model-Based PlanningCode0
A Reinforcement Learning Approach for Performance-aware Reduction in Power Consumption of Data Center Compute NodesCode0
Real Robot Challenge 2022: Learning Dexterous Manipulation from Offline Data in the Real World—0
On-demand Cold Start Frequency Reduction with Off-Policy Reinforcement Learning in Serverless Computing—0
ACRE: Actor-Critic with Reward-Preserving ExplorationCode0
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Benchmark Results

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