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 6076–6100 of 15113 papers

TitleStatusHype
SoloParkour: Constrained Reinforcement Learning for Visual Locomotion from Privileged Experience—0
SOLO: Search Online, Learn Offline for Combinatorial Optimization Problems—0
Solver-Informed RL: Grounding Large Language Models for Authentic Optimization Modeling—0
Solve Traveling Salesman Problem by Monte Carlo Tree Search and Deep Neural Network—0
Solving a New 3D Bin Packing Problem with Deep Reinforcement Learning Method—0
Solving Bayesian inverse problems with diffusion priors and off-policy RL—0
Solving Richly Constrained Reinforcement Learning through State Augmentation and Reward Penalties—0
Solving Continual Combinatorial Selection via Deep Reinforcement Learning—0
Solving Finite-Horizon MDPs via Low-Rank Tensors—0
Solving Heterogeneous General Equilibrium Economic Models with Deep Reinforcement Learning—0
Solving Math Word Problems with Double-Decoder Transformer—0
Solving Multi-Goal Robotic Tasks with Decision Transformer—0
Normalized Cut with Reinforcement Learning in Constrained Action Space—0
Solving Online Threat Screening Games using Constrained Action Space Reinforcement Learning—0
Solving optimal stopping problems with Deep Q-Learning—0
Solving Reach-Avoid-Stay Problems Using Deep Deterministic Policy Gradients—0
Solving robust MDPs as a sequence of static RL problems—0
Solving Rubik's Cube Without Tricky Sampling—0
Solving single-objective tasks by preference multi-objective reinforcement learning—0
Solving Sokoban with forward-backward reinforcement learning—0
Solving Stochastic Games—0
Solving the capacitated vehicle routing problem with timing windows using rollouts and MAX-SAT—0
Solving the Order Batching and Sequencing Problem using Deep Reinforcement Learning—0
Solving the single-track train scheduling problem via Deep Reinforcement Learning—0
Solving the Spike Feature Information Vanishing Problem in Spiking Deep Q Network with Potential Based Normalization—0
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Benchmark Results

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