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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3326–3350 of 15113 papers

TitleStatusHype
Normalization Enhances Generalization in Visual Reinforcement LearningCode0
Non-stationary Reinforcement Learning under General Function Approximation—0
Improving and Benchmarking Offline Reinforcement Learning AlgorithmsCode1
IQL-TD-MPC: Implicit Q-Learning for Hierarchical Model Predictive Control—0
Efficient Diffusion Policies for Offline Reinforcement LearningCode1
Learning for Edge-Weighted Online Bipartite Matching with Robustness GuaranteesCode1
Replicability in Reinforcement Learning—0
Let's Verify Step by StepCode4
MetaDiffuser: Diffusion Model as Conditional Planner for Offline Meta-RL—0
Policy Optimization for Continuous Reinforcement Learning—0
Robust Reinforcement Learning Objectives for Sequential Recommender SystemsCode0
Subequivariant Graph Reinforcement Learning in 3D EnvironmentsCode1
Diffusion Model is an Effective Planner and Data Synthesizer for Multi-Task Reinforcement LearningCode1
Towards a Better Understanding of Representation Dynamics under TD-learning—0
Bridging the Sim-to-Real Gap from the Information Bottleneck PerspectiveCode0
Off-Policy RL Algorithms Can be Sample-Efficient for Continuous Control via Sample Multiple ReuseCode0
Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte CarloCode1
RLAD: Reinforcement Learning from Pixels for Autonomous Driving in Urban Environments—0
RL + Model-based Control: Using On-demand Optimal Control to Learn Versatile Legged Locomotion—0
Potential-based Credit Assignment for Cooperative RL-based Testing of Autonomous Vehicles—0
MADiff: Offline Multi-agent Learning with Diffusion ModelsCode1
Future-conditioned Unsupervised Pretraining for Decision TransformerCode1
Policy Synthesis and Reinforcement Learning for Discounted LTL—0
Reinforcement Learning with Simple Sequence Priors—0
The Curious Price of Distributional Robustness in Reinforcement Learning with a Generative Model—0
Show:102550
← PrevPage 134 of 605Next →

Benchmark Results

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