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 72517300 of 15113 papers

TitleStatusHype
Experience Replay More When It's a Key Transition in Deep Reinforcement Learning0
Efficient Wasserstein and Sinkhorn Policy Optimization0
Adaptive Graph Capsule Convolutional Networks0
Decoupling Strategy and Surface Realization for Task-oriented Dialogues0
Evolution Strategies as an Alternate Learning method for Hierarchical Reinforcement Learning0
Learning Controllable Elements Oriented Representations for Reinforcement Learning0
Interpreting Reinforcement Policies through Local Behaviors0
A Flexible Measurement of Diversity in Datasets with Random Network Distillation0
A General Theory of Relativity in Reinforcement Learning0
HyperDQN: A Randomized Exploration Method for Deep Reinforcement LearningCode1
Greedy-based Value Representation for Efficient Coordination in Multi-agent Reinforcement Learning0
Efficient Reinforcement Learning Experimentation in PyTorch0
Exploring the Robustness of Distributional Reinforcement Learning against Noisy State Observations0
Decentralized Cooperative Multi-Agent Reinforcement Learning with Exploration0
CausalDyna: Improving Generalization of Dyna-style Reinforcement Learning via Counterfactual-Based Data Augmentation0
Adaptive Q-learning for Interaction-Limited Reinforcement Learning0
Evolutionary Diversity Optimization with Clustering-based Selection for Reinforcement Learning0
Assessing Deep Reinforcement Learning Policies via Natural Corruptions at the Edge of Imperceptibility0
Auto-Encoding Inverse Reinforcement Learning0
Better state exploration using action sequence equivalence0
Deep Ensemble Policy Learning0
A Principled Permutation Invariant Approach to Mean-Field Multi-Agent Reinforcement Learning0
Bayesian Exploration for Lifelong Reinforcement Learning0
An Optics Controlling Environment and Reinforcement Learning Benchmarks0
Fully Decentralized Model-based Policy Optimization with Networked Agents0
Combinatorial Reinforcement Learning Based Scheduling for DNN Execution on Edge0
Hypothesis Driven Coordinate Ascent for Reinforcement Learning0
Adversarial Style Transfer for Robust Policy Optimization in Reinforcement Learning0
Distributional Perturbation for Efficient Exploration in Distributional Reinforcement Learning0
Fourier Features in Reinforcement Learning with Neural NetworksCode0
AARL: Automated Auxiliary Loss for Reinforcement Learning0
Multi-Agent Reinforcement Learning with Shared Resource in Inventory Management0
Rewardless Open-Ended Learning (ROEL)0
The guide and the explorer: smart agents for resource-limited iterated batch reinforcement learning0
Semi-supervised Offline Reinforcement Learning with Pre-trained Decision Transformers0
Offline Reinforcement Learning with Resource Constrained Online Deployment0
Offline Reinforcement Learning with In-sample Q-LearningCode1
Pretraining for Language Conditioned Imitation with Transformers0
Reasoning With Hierarchical Symbols: Reclaiming Symbolic Policies For Visual Reinforcement Learning0
PDQN - A Deep Reinforcement Learning Method for Planning with Long Delays: Optimization of Manufacturing Dispatching0
Theoretical understanding of adversarial reinforcement learning via mean-field optimal control0
Pareto Policy Adaptation0
SPP-RL: State Planning Policy Reinforcement Learning0
Reinforcement Learning State Estimation for High-Dimensional Nonlinear Systems0
Towards Understanding Distributional Reinforcement Learning: Regularization, Optimization, Acceleration and Sinkhorn Algorithm0
^2-exploration for Reinforcement Learning0
MOBA: Multi-teacher Model Based Reinforcement Learning0
Rethinking Pareto Approaches in Constrained Reinforcement Learning0
Reinforcement Learning with Ex-Post Max-Min Fairness0
Weakly-Supervised Learning of Disentangled and Interpretable Skills for Hierarchical Reinforcement Learning0
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Benchmark Results

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