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 10301–10325 of 15113 papers

TitleStatusHype
Efficient Exploration through Intrinsic Motivation Learning for Unsupervised Subgoal Discovery in Model-Free Hierarchical Reinforcement Learning—0
Efficient Exploration Using Extra Safety Budget in Constrained Policy Optimization—0
Efficient Exploration via Epistemic-Risk-Seeking Policy Optimization—0
Efficient Implementation of Reinforcement Learning over Homomorphic Encryption—0
Uncertainty Quantification and Exploration for Reinforcement Learning—0
Efficient Inference and Exploration for Reinforcement Learning—0
Efficient Learning of High Level Plans from Play—0
Efficient Learning of Safe Driving Policy via Human-AI Copilot Optimization—0
Efficient Learning of Voltage Control Strategies via Model-based Deep Reinforcement Learning—0
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
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Benchmark Results

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