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 7976–8000 of 15113 papers

TitleStatusHype
DESTA: A Framework for Safe Reinforcement Learning with Markov Games of Intervention—0
A Subgame Perfect Equilibrium Reinforcement Learning Approach to Time-inconsistent Problems—0
The ODE Method for Asymptotic Statistics in Stochastic Approximation and Reinforcement Learning—0
Stabilising viscous extensional flows using Reinforcement LearningCode0
APPTeK: Agent-Based Predicate Prediction in Temporal Knowledge Graphs—0
Model based Multi-agent Reinforcement Learning with Tensor Decompositions—0
Transfer learning with causal counterfactual reasoning in Decision Transformers—0
Reinforcement Learning in Linear MDPs: Constant Regret and Representation Selection—0
Reinforcement Learning in Factored Action Spaces using Tensor Decompositions—0
Towards Hyperparameter-free Policy Selection for Offline Reinforcement LearningCode0
The Difficulty of Passive Learning in Deep Reinforcement Learning—0
Multi-Agent Advisor Q-LearningCode0
Fragment-based Sequential Translation for Molecular Optimization—0
Average-Reward Learning and Planning with Options—0
Distributional Reinforcement Learning for Multi-Dimensional Reward FunctionsCode0
Accelerating Distributed Deep Reinforcement Learning by In-Network Experience Sampling—0
EnTRPO: Trust Region Policy Optimization Method with Entropy Regularization—0
Neural PPO-Clip Attains Global Optimality: A Hinge Loss Perspective—0
Automating Control of Overestimation Bias for Reinforcement Learning—0
Learning Robust Controllers Via Probabilistic Model-Based Policy Search—0
Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey—0
Distributed Multi-Agent Deep Reinforcement Learning Framework for Whole-building HVAC Control—0
Learning What to Memorize: Using Intrinsic Motivation to Form Useful Memory in Partially Observable Reinforcement Learning—0
Can Q-Learning be Improved with Advice?—0
Common Information based Approximate State Representations in Multi-Agent Reinforcement Learning—0
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Benchmark Results

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