Learning Exploration Strategies with Meta-Reinforcement Learning

Channel:
United States Simons Institute for the Theory of Computing
Subscribers:
68,700
Published on ● Video Link: https://www.youtube.com/watch?v=xS6T_76mRYk


Duration: 31:22
3,035 views
62

Chelsea Finn (Stanford University)
https://simons.berkeley.edu/talks/tbd-214
Deep Reinforcement Learning



Other Videos By Simons Institute for the Theory of Computing


2020-10-01Language as a Scaffold for Reinforcement Learning
2020-09-30Discussion: Optimization
2020-09-30Generalizing the Projected Bellman Error Objective for Nonlinear Value Estimation
2020-09-30Stabilizing Q-learning with Weighted Bellman Losses
2020-09-30Munchausen Reinforcement Learning
2020-09-30Adaptive Approximate Policy Iteration
2020-09-30On Distance Approximation for Graph Properties
2020-09-29Discussion: Exploration
2020-09-29PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learning
2020-09-29Temporally-Extended ε-Greedy Exploration
2020-09-29Learning Exploration Strategies with Meta-Reinforcement Learning
2020-09-29Behaviour Suite for Reinforcement Learning
2020-09-28Discussion: Offline Reinforcement Learning
2020-09-28Attacking the Off-Policy Problem With Duality
2020-09-28Offline Deep Reinforcement Learning Algorithms
2020-09-28Learning from the Past Without Great Exploration
2020-09-28MOPO: Model-Based Offline Policy Optimization
2020-09-25Pedagogical Talk: Frameworks for Information-Computation Tradeoffs
2020-09-25Reducibility and Statistical-Computational Gaps from Secret Leakage
2020-09-25Berkeley in the 80s, Episode 4: Andrew Yao
2020-09-25Free Energy Wells and Overlap Gap Property in Sparse PCA


Tags:
Chelsea Finn
Simons Institute
Theory of Computing
Theory of Computation
Theoretical Computer Science
Computer Science
UC Berkeley
Deep Reinforcement Learning