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 6826–6850 of 15113 papers

TitleStatusHype
Efficient LSTM Training with Eligibility Traces—0
Efficiently Breaking the Curse of Horizon in Off-Policy Evaluation with Double Reinforcement Learning—0
Efficiently Learning Small Policies for Locomotion and Manipulation—0
Efficiently Training On-Policy Actor-Critic Networks in Robotic Deep Reinforcement Learning with Demonstration-like Sampled Exploration—0
Efficient meta reinforcement learning via meta goal generation—0
MGHRL: Meta Goal-generation for Hierarchical Reinforcement Learning—0
Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation—0
Efficient Model-Based Multi-Agent Mean-Field Reinforcement Learning—0
Efficient model-based reinforcement learning for approximate online optimal—0
Efficient Model-Free Reinforcement Learning Using Gaussian Process—0
QMP: Q-switch Mixture of Policies for Multi-Task Behavior Sharing—0
Efficient Navigation of Colloidal Robots in an Unknown Environment via Deep Reinforcement Learning—0
BNAS:An Efficient Neural Architecture Search Approach Using Broad Scalable Architecture—0
Efficient Neural Clause-Selection Reinforcement—0
Efficient Off-Policy Safe Reinforcement Learning Using Trust Region Conditional Value at Risk—0
Efficient Online RL Fine Tuning with Offline Pre-trained Policy Only—0
Efficient PAC Reinforcement Learning in Regular Decision Processes—0
Efficient Performance Bounds for Primal-Dual Reinforcement Learning from Demonstrations—0
Efficient Planning in Combinatorial Action Spaces with Applications to Cooperative Multi-Agent Reinforcement Learning—0
Efficient Planning under Partial Observability with Unnormalized Q Functions and Spectral Learning—0
Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents—0
Efficient Policy Learning for Non-Stationary MDPs under Adversarial Manipulation—0
Efficient Poverty Mapping using Deep Reinforcement Learning—0
Efficient Preference-Based Reinforcement Learning Using Learned Dynamics Models—0
Reinforcement Learning for Causal Discovery without Acyclicity Constraints—0
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Benchmark Results

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