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 4641–4650 of 15113 papers

TitleStatusHype
How the level sampling process impacts zero-shot generalisation in deep reinforcement learning—0
Constraint-Conditioned Policy Optimization for Versatile Safe Reinforcement Learning—0
LESSON: Learning to Integrate Exploration Strategies for Reinforcement Learning via an Option FrameworkCode0
Resilient Legged Local Navigation: Learning to Traverse with Compromised Perception End-to-End—0
Safe Exploration in Reinforcement Learning: A Generalized Formulation and Algorithms—0
Optimal Control of District Cooling Energy Plant with Reinforcement Learning and MPC—0
RTDK-BO: High Dimensional Bayesian Optimization with Reinforced Transformer Deep kernels—0
Neural architecture impact on identifying temporally extended Reinforcement Learning tasks—0
Proximal Policy Optimization-Based Reinforcement Learning Approach for DC-DC Boost Converter Control: A Comparative Evaluation Against Traditional Control Techniques—0
Searching for High-Value Molecules Using Reinforcement Learning and Transformers—0
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Benchmark Results

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