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 3031–3040 of 15113 papers

TitleStatusHype
Iterative Reward Shaping using Human Feedback for Correcting Reward MisspecificationCode0
A reinforcement learning based construction material supply strategy using robotic crane and computer vision for building reconstruction after an earthquake—0
Benchmarking Robustness and Generalization in Multi-Agent Systems: A Case Study on Neural MMO—0
Improving Generalization in Reinforcement Learning Training Regimes for Social Robot NavigationCode0
On the improvement of model-predictive controllers—0
Recent Progress in Energy Management of Connected Hybrid Electric Vehicles Using Reinforcement Learning—0
Prompt to Transfer: Sim-to-Real Transfer for Traffic Signal Control with Prompt LearningCode1
Target-independent XLA optimization using Reinforcement Learning—0
Reinforcement Learning-based Optimal Control and Software Rejuvenation for Safe and Efficient UAV Navigation—0
JAX-LOB: A GPU-Accelerated limit order book simulator to unlock large scale reinforcement learning for trading—0
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Benchmark Results

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