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 3911–3920 of 15113 papers

TitleStatusHype
Sample Efficient Deep Reinforcement Learning via Local Planning—0
Do Embodied Agents Dream of Pixelated Sheep: Embodied Decision Making using Language Guided World ModellingCode1
Constrained Policy Optimization with Explicit Behavior Density for Offline Reinforcement LearningCode0
Turbulence control in plane Couette flow using low-dimensional neural ODE-based models and deep reinforcement learning—0
SaFormer: A Conditional Sequence Modeling Approach to Offline Safe Reinforcement Learning—0
Beyond Exponentially Fast Mixing in Average-Reward Reinforcement Learning via Multi-Level Monte Carlo Actor-Critic—0
Towards Learning Rubik's Cube with N-tuple-based Reinforcement Learning—0
STEERING: Stein Information Directed Exploration for Model-Based Reinforcement Learning—0
A Memory Efficient Deep Reinforcement Learning Approach For Snake Game Autonomous Agents—0
Exploring Deep Reinforcement Learning for Holistic Smart Building Control—0
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Benchmark Results

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