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Deep RL

3,861 papers

Papers per year

Papers

On Reinforcement Learning with Adversarial Corruption and Its Application to Block MDP ICML 2021
Contextual policy transfer in reinforcement learning domains via deep mixtures-of-experts UAI 2021
Replay-Guided Adversarial Environment Design NIPS 2021
Commission Fee is not Enough: A Hierarchical Reinforced Framework for Portfolio Management AAAI 2021
Formal verification of neural networks for safety-critical tasks in deep reinforcement learning UAI 2021
Deciding What to Learn: A Rate-Distortion Approach ICML 2021
RL for Latent MDPs: Regret Guarantees and a Lower Bound NIPS 2021
RewardsOfSum: Exploring Reinforcement Learning Rewards for Summarisation ACL 2021
Hierarchical Object-to-Zone Graph for Object Navigation ICCV 2021
Model-free Safe Control for Zero-Violation Reinforcement Learning CORL 2021
Model-Based Reinforcement Learning via Latent-Space Collocation ICML 2021
A Provably Efficient Sample Collection Strategy for Reinforcement Learning NIPS 2021
TempoRL: Learning When to Act ICML 2021
Stochastic Actor-Executor-Critic for Image-to-Image Translation IJCAI 2021
Urban Driver: Learning to Drive from Real-world Demonstrations Using Policy Gradients CORL 2021
Improving Generalization in Meta-RL with Imaginary Tasks from Latent Dynamics Mixture NIPS 2021
On Reward-Free RL with Kernel and Neural Function Approximations: Single-Agent MDP and Markov Game ICML 2021
Learning to Plan Optimistically: Uncertainty-Guided Deep Exploration via Latent Model Ensembles CORL 2021
Exploration of Unknown Environments Using Deep Reinforcement Learning AAAI 2021
Safe Reinforcement Learning of Control-Affine Systems with Vertex Networks L4DC 2021
Reinforcement Learning-based Product Delivery Frequency Control AAAI 2021
Improved Corruption Robust Algorithms for Episodic Reinforcement Learning ICML 2021
Revisiting Peng’s Q($λ$) for Modern Reinforcement Learning ICML 2021
Off-Policy Confidence Sequences ICML 2021
Non-asymptotic Convergence of Adam-type Reinforcement Learning Algorithms under Markovian Sampling AAAI 2021