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

Showing 79017950 of 15113 papers

TitleStatusHype
On the Global Convergence of Risk-Averse Policy Gradient Methods with Expected Conditional Risk Measures0
On the impact of MDP design for Reinforcement Learning agents in Resource Management0
On the Importance of Critical Period in Multi-stage Reinforcement Learning0
On the improvement of model-predictive controllers0
On the Limitations of Markovian Rewards to Express Multi-Objective, Risk-Sensitive, and Modal Tasks0
On the Linear convergence of Natural Policy Gradient Algorithm0
On the Mechanism of Reasoning Pattern Selection in Reinforcement Learning for Language Models0
On the Modeling Capabilities of Large Language Models for Sequential Decision Making0
On the Near-Optimality of Local Policies in Large Cooperative Multi-Agent Reinforcement Learning0
Opportunities and Challenges from Using Animal Videos in Reinforcement Learning for Navigation0
Model-Based Reinforcement Learning with a Generative Model is Minimax Optimal0
On the Power of Multitask Representation Learning in Linear MDP0
On the Power of Pre-training for Generalization in RL: Provable Benefits and Hardness0
On the Practical Consistency of Meta-Reinforcement Learning Algorithms0
On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning0
On the Reduction of Variance and Overestimation of Deep Q-Learning0
On the Relationship Between Active Inference and Control as Inference0
On the Relationship Between the OpenAI Evolution Strategy and Stochastic Gradient Descent0
On the Robustness of Controlled Deep Reinforcement Learning for Slice Placement0
On the Robustness of Deep Reinforcement Learning in IRS-Aided Wireless Communications Systems0
On the Role of Discount Factor in Offline Reinforcement Learning0
On the role of planning in model-based deep reinforcement learning0
On the Role of the Action Space in Robot Manipulation Learning and Sim-to-Real Transfer0
On the Sample Complexity of Actor-Critic Method for Reinforcement Learning with Function Approximation0
The Curse of Passive Data Collection in Batch Reinforcement Learning0
On the Sample Complexity of Reinforcement Learning with Policy Space Generalization0
On the Sample Complexity of Vanilla Model-Based Offline Reinforcement Learning with Dependent Samples0
On the Sample Efficiency of Abstractions and Potential-Based Reward Shaping in Reinforcement Learning0
On the Search for Feedback in Reinforcement Learning0
On the Stability and Convergence of Robust Adversarial Reinforcement Learning: A Case Study on Linear Quadratic Systems0
On the Stability of Random Matrix Product with Markovian Noise: Application to Linear Stochastic Approximation and TD Learning0
On the Statistical Complexity for Offline and Low-Adaptive Reinforcement Learning with Structures0
On the Statistical Efficiency of Reward-Free Exploration in Non-Linear RL0
On the Stochastic (Variance-Reduced) Proximal Gradient Method for Regularized Expected Reward Optimization0
On the Theory of Reinforcement Learning with Once-per-Episode Feedback0
On the use of Deep Autoencoders for Efficient Embedded Reinforcement Learning0
On the use of feature-maps and parameter control for improved quality-diversity meta-evolution0
On the Verge of Solving Rocket League using Deep Reinforcement Learning and Sim-to-sim Transfer0
On the Weaknesses of Reinforcement Learning for Neural Machine Translation0
On Thompson Sampling for Smoother-than-Lipschitz Bandits0
Ontology-Enhanced Decision-Making for Autonomous Agents in Dynamic and Partially Observable Environments0
On Trade-offs of Image Prediction in Visual Model-Based Reinforcement Learning0
On Training Flexible Robots using Deep Reinforcement Learning0
On Transforming Reinforcement Learning by Transformer: The Development Trajectory0
On Value Functions and the Agent-Environment Boundary0
On Wasserstein Reinforcement Learning and the Fokker-Planck equation0
OPAC: Opportunistic Actor-Critic0
OPAL: Offline Primitive Discovery for Accelerating Offline Reinforcement Learning0
OPEB: Open Physical Environment Benchmark for Artificial Intelligence0
Open-Ended Learning Strategies for Learning Complex Locomotion Skills0
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Benchmark Results

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