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Deep RL

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Papers

Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design NIPS 2020
POMDPs in Continuous Time and Discrete Spaces NIPS 2020
Promoting Stochasticity for Expressive Policies via a Simple and Efficient Regularization Method NIPS 2020
Attention-Gated Brain Propagation: How the brain can implement reward-based error backpropagation NIPS 2020
Weakly-Supervised Reinforcement Learning for Controllable Behavior NIPS 2020
A Local Temporal Difference Code for Distributional Reinforcement Learning NIPS 2020
Learning to Optimize Variational Quantum Circuits to Solve Combinatorial Problems AAAI 2020
Discretizing Continuous Action Space for On-Policy Optimization AAAI 2020
Contextual-Bandit Based Personalized Recommendation with Time-Varying User Interests AAAI 2020
Accelerating Ranking in E-Commerce Search Engines through Contextual Factor Selection AAAI 2020
Upper Confidence Primal-Dual Reinforcement Learning for CMDP with Adversarial Loss NIPS 2020
Self-Imitation Learning via Generalized Lower Bound Q-learning NIPS 2020
Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning NIPS 2020
Knowledge Transfer in Multi-Task Deep Reinforcement Learning for Continuous Control NIPS 2020
Predictive Information Accelerates Learning in RL NIPS 2020
Bandit Linear Control NIPS 2020
An Equivalence between Loss Functions and Non-Uniform Sampling in Experience Replay NIPS 2020
Multi-step Greedy Reinforcement Learning Algorithms ICML 2020
DISK: Learning local features with policy gradient NIPS 2020
Reinforcement Mechanism Design: With Applications to Dynamic Pricing in Sponsored Search Auctions AAAI 2020
A Learning Based Branch and Bound for Maximum Common Subgraph Related Problems AAAI 2020
Metareasoning in Modular Software Systems: On-the-Fly Configuration Using Reinforcement Learning with Rich Contextual Representations AAAI 2020
Transfer Value Iteration Networks AAAI 2020
Generating Persona Consistent Dialogues by Exploiting Natural Language Inference AAAI 2020
Point-Based Methods for Model Checking in Partially Observable Markov Decision Processes AAAI 2020