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 5701–5725 of 15113 papers

TitleStatusHype
Sample-efficient policy learning in multi-agent Reinforcement Learning via meta-learning—0
Sample efficient Quality Diversity for neural continuous control—0
Sample-Efficient Reinforcement Learning through Transfer and Architectural Priors—0
Sample Efficient Reinforcement Learning by Automatically Learning to Compose Subtasks—0
Sample-Efficient Reinforcement Learning for Linearly-Parameterized MDPs with a Generative Model—0
Reinforcement Learning from Partial Observation: Linear Function Approximation with Provable Sample Efficiency—0
Sample-efficient Reinforcement Learning in Robotic Table Tennis—0
Sample Efficient Reinforcement Learning In Continuous State Spaces: A Perspective Beyond Linearity—0
Sample-Efficient Reinforcement Learning in the Presence of Exogenous Information—0
Sample-Efficient Reinforcement Learning Is Feasible for Linearly Realizable MDPs with Limited Revisiting—0
Sample-Efficient Reinforcement Learning of Undercomplete POMDPs—0
Sample-Efficient Reinforcement Learning of Partially Observable Markov Games—0
Sample-Efficient Reinforcement Learning of Koopman eNMPC—0
Sample-efficient reinforcement learning using deep Gaussian processes—0
Sample Efficient Reinforcement Learning via Low-Rank Matrix Estimation—0
Sample-Efficient Reinforcement Learning via Counterfactual-Based Data Augmentation—0
Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion—0
Sample Efficient Reinforcement Learning with REINFORCE—0
Sample-Efficient Reinforcement Learning with loglog(T) Switching Cost—0
Sample-Efficient Robust Multi-Agent Reinforcement Learning in the Face of Environmental Uncertainty—0
Sample-efficient Safe Learning for Online Nonlinear Control with Control Barrier Functions—0
Sample Efficient Social Navigation Using Inverse Reinforcement Learning—0
Sampling from Energy-based Policies using Diffusion—0
Sampling Strategies for GAN Synthetic Data—0
Sampling Through the Lens of Sequential Decision Making—0
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Benchmark Results

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