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Found 12 papers, 2 papers with code
SoftTreeMax: Exponential Variance Reduction in Policy Gradient via Tree Search
We prove that the resulting variance decays exponentially with the planning horizon as a function of the expansion policy.
SoftTreeMax: Policy Gradient with Tree Search
This allows us to reduce the variance of gradients by three orders of magnitude and to benefit from better sample complexity compared with standard policy gradient.
Reinforcement Learning with a Terminator
We learn the parameters of the TerMDP and leverage the structure of the estimation problem to provide state-wise confidence bounds.
Planning and Learning with Adaptive Lookahead
Some of the most powerful reinforcement learning frameworks use planning for action selection.
On Covariate Shift of Latent Confounders in Imitation and Reinforcement Learning
We analyze the limitations of learning from such data with and without external reward, and propose an adjustment of standard imitation learning algorithms to fit this setup.
Improve Agents without Retraining: Parallel Tree Search with Off-Policy Correction
We first discover and analyze a counter-intuitive phenomenon: action selection through TS and a pre-trained value function often leads to lower performance compared to the original pre-trained agent, even when having access to the exact state and reward in future steps.
Automatic Representation for Lifetime Value Recommender Systems
The LTV approach considers the future implications of the item recommendation, and seeks to maximize the cumulative gain over time.
Consistent On-Line Off-Policy Evaluation
The problem of on-line off-policy evaluation (OPE) has been actively studied in the last decade due to its importance both as a stand-alone problem and as a module in a policy improvement scheme.
Generalized Emphatic Temporal Difference Learning: Bias-Variance Analysis
We consider the off-policy evaluation problem in Markov decision processes with function approximation.
Emphatic TD Bellman Operator is a Contraction
Recently, \citet{SuttonMW15} introduced the emphatic temporal differences (ETD) algorithm for off-policy evaluation in Markov decision processes.
Off-policy evaluation for MDPs with unknown structure
Off-policy learning in dynamic decision problems is essential for providing strong evidence that a new policy is better than the one in use.
Contextual Markov Decision Processes
The objective is to learn a strategy that maximizes the accumulated reward across all contexts.
