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

Showing 33013350 of 15113 papers

TitleStatusHype
State Regularized Policy Optimization on Data with Dynamics Shift0
Survival Instinct in Offline Reinforcement Learning0
Risk-Aware Reward Shaping of Reinforcement Learning Agents for Autonomous DrivingCode0
A General Perspective on Objectives of Reinforcement Learning0
A Novel Multi-Agent Deep RL Approach for Traffic Signal Control0
Action-Evolution Petri Nets: a Framework for Modeling and Solving Dynamic Task Assignment Problems0
Seizing Serendipity: Exploiting the Value of Past Success in Off-Policy Actor-CriticCode1
For SALE: State-Action Representation Learning for Deep Reinforcement LearningCode1
Cycle Consistency Driven Object Discovery0
Learning to Stabilize Online Reinforcement Learning in Unbounded State SpacesCode0
Improving the generalizability and robustness of large-scale traffic signal control0
Reinforcement Learning with General Utilities: Simpler Variance Reduction and Large State-Action Space0
A Modular Test Bed for Reinforcement Learning Incorporation into Industrial Applications0
Hyperparameters in Reinforcement Learning and How To Tune Them0
Interpretable and Explainable Logical Policies via Neurally Guided Symbolic Abstraction0
Efficient Reinforcement Learning with Impaired Observability: Learning to Act with Delayed and Missing State Observations0
Deep Q-Learning versus Proximal Policy Optimization: Performance Comparison in a Material Sorting Task0
An Architecture for Deploying Reinforcement Learning in Industrial Environments0
Thought Cloning: Learning to Think while Acting by Imitating Human ThinkingCode2
Heterogeneous Knowledge for Augmented Modular Reinforcement Learning0
Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding0
TorchRL: A data-driven decision-making library for PyTorchCode4
Identifiability and Generalizability in Constrained Inverse Reinforcement LearningCode0
Achieving Fairness in Multi-Agent Markov Decision Processes Using Reinforcement Learning0
Safe Offline Reinforcement Learning with Real-Time Budget ConstraintsCode1
Normalization Enhances Generalization in Visual Reinforcement LearningCode0
Non-stationary Reinforcement Learning under General Function Approximation0
Improving and Benchmarking Offline Reinforcement Learning AlgorithmsCode1
IQL-TD-MPC: Implicit Q-Learning for Hierarchical Model Predictive Control0
Efficient Diffusion Policies for Offline Reinforcement LearningCode1
Learning for Edge-Weighted Online Bipartite Matching with Robustness GuaranteesCode1
Replicability in Reinforcement Learning0
Let's Verify Step by StepCode4
MetaDiffuser: Diffusion Model as Conditional Planner for Offline Meta-RL0
Policy Optimization for Continuous Reinforcement Learning0
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-learning0
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 Environments0
RL + Model-based Control: Using On-demand Optimal Control to Learn Versatile Legged Locomotion0
Potential-based Credit Assignment for Cooperative RL-based Testing of Autonomous Vehicles0
MADiff: Offline Multi-agent Learning with Diffusion ModelsCode1
Future-conditioned Unsupervised Pretraining for Decision TransformerCode1
Policy Synthesis and Reinforcement Learning for Discounted LTL0
Reinforcement Learning with Simple Sequence Priors0
The Curious Price of Distributional Robustness in Reinforcement Learning with a Generative Model0
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Benchmark Results

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