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Deep RL

3861 directly classified papers

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Papers

Bridging Imagination and Reality for Model-Based Deep Reinforcement Learning NIPS 2020
Is Long Horizon RL More Difficult Than Short Horizon RL? NIPS 2020
Self-Paced Deep Reinforcement Learning NIPS 2020
Steady State Analysis of Episodic Reinforcement Learning NIPS 2020
Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver? NIPS 2020
An Efficient Asynchronous Method for Integrating Evolutionary and Gradient-based Policy Search NIPS 2020
Trust the Model When It Is Confident: Masked Model-based Actor-Critic NIPS 2020
Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning NIPS 2020
RD$^2$: Reward Decomposition with Representation Decomposition NIPS 2020
Instance-based Generalization in Reinforcement Learning NIPS 2020
Task-agnostic Exploration in Reinforcement Learning NIPS 2020
Provably Efficient Reward-Agnostic Navigation with Linear Value Iteration NIPS 2020
Softmax Deep Double Deterministic Policy Gradients NIPS 2020
Sample Efficient Reinforcement Learning via Low-Rank Matrix Estimation NIPS 2020
Leverage the Average: an Analysis of KL Regularization in Reinforcement Learning NIPS 2020
Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model NIPS 2020
Reward Propagation Using Graph Convolutional Networks NIPS 2020
Trajectory-wise Multiple Choice Learning for Dynamics Generalization in Reinforcement Learning NIPS 2020
Sparse Graphical Memory for Robust Planning NIPS 2020
Online Decision Based Visual Tracking via Reinforcement Learning NIPS 2020
Agnostic $Q$-learning with Function Approximation in Deterministic Systems: Near-Optimal Bounds on Approximation Error and Sample Complexity NIPS 2020
Effective Diversity in Population Based Reinforcement Learning NIPS 2020
Simultaneously Learning Stochastic and Adversarial Episodic MDPs with Known Transition NIPS 2020
A Unified Switching System Perspective and Convergence Analysis of Q-Learning Algorithms NIPS 2020
Bias no more: high-probability data-dependent regret bounds for adversarial bandits and MDPs NIPS 2020