Search Results for author:
Found 9 papers, 7 papers with code
Model-Based Reinforcement Learning for Atari
We describe Simulated Policy Learning (SimPLe), a complete model-based deep RL algorithm based on video prediction models and present a comparison of several model architectures, including a novel architecture that yields the best results in our setting.
Ranked #12 on
Atari Games 100k
on Atari 100k
Uncertainty - sensitive learning and planning with ensembles
Notably, our method performs well in environments with sparse rewards where standard $TD(1)$ backups fail.
Uncertainty-sensitive Learning and Planning with Ensembles
The former manifests itself through the use of value function, while the latter is powered by a tree search planner.
Model Based Reinforcement Learning for Atari
We describe Simulated Policy Learning (SimPLe), a complete model-based deep RL algorithm based on video prediction models and present a comparison of several model architectures, including a novel architecture that yields the best results in our setting.
Trust, but verify: model-based exploration in sparse reward environments
We propose $\textit{trust-but-verify}$ (TBV) mechanism, a new method which uses model uncertainty estimates to guide exploration.
Model-based Reinforcement Learning
reinforcement-learning
+1
Structure and randomness in planning and reinforcement learning
Planning in large state spaces inevitably needs to balance depth and breadth of the search.
Subgoal Search For Complex Reasoning Tasks
In this paper, we implement kSubS using a transformer-based subgoal module coupled with the classical best-first search framework.
Thor: Wielding Hammers to Integrate Language Models and Automated Theorem Provers
Thor increases a language model's success rate on the PISA dataset from $39\%$ to $57\%$, while solving $8. 2\%$ of problems neither language models nor automated theorem provers are able to solve on their own.
Ranked #2 on
Automated Theorem Proving
on miniF2F-test
Fast and Precise: Adjusting Planning Horizon with Adaptive Subgoal Search
Complex reasoning problems contain states that vary in the computational cost required to determine a good action plan.
