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 3471–3480 of 15113 papers

TitleStatusHype
Sample-efficient Model-based Reinforcement Learning for Quantum ControlCode1
Using Offline Data to Speed Up Reinforcement Learning in Procedurally Generated EnvironmentsCode0
Feasible Policy Iteration for Safe Reinforcement Learning—0
Cooperative Multi-Agent Reinforcement Learning for Inventory Management—0
Benchmarking Actor-Critic Deep Reinforcement Learning Algorithms for Robotics Control with Action ConstraintsCode1
Provably Feedback-Efficient Reinforcement Learning via Active Reward Learning—0
An adaptive safety layer with hard constraints for safe reinforcement learning in multi-energy management systems—0
MDDL: A Framework for Reinforcement Learning-based Position Allocation in Multi-Channel Feed—0
TreeC: a method to generate interpretable energy management systems using a metaheuristic algorithmCode0
Bandit-Based Policy Invariant Explicit Shaping for Incorporating External Advice in Reinforcement Learning—0
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Benchmark Results

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