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 9101–9125 of 15113 papers

TitleStatusHype
Interpretable Meta-Reinforcement Learning with Actor-Critic Method—0
Learning to Dynamically Select Between Reward Shaping Signals—0
Genetic Soft Updates for Policy Evolution in Deep Reinforcement Learning—0
Explore with Dynamic Map: Graph Structured Reinforcement Learning—0
Addressing Distribution Shift in Online Reinforcement Learning with Offline Datasets—0
Bounded Myopic Adversaries for Deep Reinforcement Learning Agents—0
A Robust Fuel Optimization Strategy For Hybrid Electric Vehicles: A Deep Reinforcement Learning Based Continuous Time Design Approach—0
Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms—0
Divide-and-Conquer Monte Carlo Tree Search—0
Hindsight Curriculum Generation Based Multi-Goal Experience Replay—0
Combining Imitation and Reinforcement Learning with Free Energy Principle—0
Interpretable Reinforcement Learning With Neural Symbolic Logic—0
Learning to communicate through imagination with model-based deep multi-agent reinforcement learning—0
Hellinger Distance Constrained Regression—0
FSV: Learning to Factorize Soft Value Function for Cooperative Multi-Agent Reinforcement Learning—0
Explicit Pareto Front Optimization for Constrained Reinforcement Learning—0
Deep Coherent Exploration For Continuous Control—0
Factored Action Spaces in Deep Reinforcement Learning—0
Addressing Extrapolation Error in Deep Offline Reinforcement Learning—0
Playing Atari with Capsule Networks: A systematic comparison of CNN and CapsNets-based agents.—0
Trust, but verify: model-based exploration in sparse reward environmentsCode0
MQES: Max-Q Entropy Search for Efficient Exploration in Continuous Reinforcement Learning—0
Structure and randomness in planning and reinforcement learningCode0
PAC-Bayesian Randomized Value Function with Informative Prior—0
PERIL: Probabilistic Embeddings for hybrid Meta-Reinforcement and Imitation Learning—0
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Benchmark Results

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