(15 events) Timezone: America/Los_Angeles
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Poster
Tue Dec 10 05:30 PM -- 07:30 PM (PST) @ East Exhibition Hall B + C #201
Convergent Policy Optimization for Safe Reinforcement Learning
Poster
Tue Dec 10 05:30 PM -- 07:30 PM (PST) @ East Exhibition Hall B + C #202
Experience Replay for Continual Learning
Poster
Tue Dec 10 05:30 PM -- 07:30 PM (PST) @ East Exhibition Hall B + C #203
Exploration via Hindsight Goal Generation
Poster
Tue Dec 10 05:30 PM -- 07:30 PM (PST) @ East Exhibition Hall B + C #204
Hindsight Credit Assignment
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Paper]
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3 min Video]
Poster
Tue Dec 10 05:30 PM -- 07:30 PM (PST) @ East Exhibition Hall B + C #205
Imitation Learning from Observations by Minimizing Inverse Dynamics Disagreement
Poster
Tue Dec 10 05:30 PM -- 07:30 PM (PST) @ East Exhibition Hall B + C #206
Importance Resampling for Off-policy Prediction
Poster
Tue Dec 10 05:30 PM -- 07:30 PM (PST) @ East Exhibition Hall B + C #207
Learning Compositional Neural Programs with Recursive Tree Search and Planning
Poster
Tue Dec 10 05:30 PM -- 07:30 PM (PST) @ East Exhibition Hall B + C #208
Multi-View Reinforcement Learning
Poster
Tue Dec 10 05:30 PM -- 07:30 PM (PST) @ East Exhibition Hall B + C #209
Real-Time Reinforcement Learning
Poster
Tue Dec 10 05:30 PM -- 07:30 PM (PST) @ East Exhibition Hall B + C #210
Reconciling λ-Returns with Experience Replay
Poster
Tue Dec 10 05:30 PM -- 07:30 PM (PST) @ East Exhibition Hall B + C #211
Regret Minimization for Reinforcement Learning by Evaluating the Optimal Bias Function
Poster
Tue Dec 10 05:30 PM -- 07:30 PM (PST) @ East Exhibition Hall B + C #212
Sample-Efficient Deep Reinforcement Learning via Episodic Backward Update
Poster
Tue Dec 10 05:30 PM -- 07:30 PM (PST) @ East Exhibition Hall B + C #213
Staying up to Date with Online Content Changes Using Reinforcement Learning for Scheduling
Poster
Tue Dec 10 05:30 PM -- 07:30 PM (PST) @ East Exhibition Hall B + C #214
Trust Region-Guided Proximal Policy Optimization
Poster
Tue Dec 10 05:30 PM -- 07:30 PM (PST) @ East Exhibition Hall B + C #215
Using a Logarithmic Mapping to Enable Lower Discount Factors in Reinforcement Learning