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Found 5 papers, 0 papers with code
Acceleration in Policy Optimization
We work towards a unifying paradigm for accelerating policy optimization methods in reinforcement learning (RL) by integrating foresight in the policy improvement step via optimistic and adaptive updates.
Selective Credit Assignment
Efficient credit assignment is essential for reinforcement learning algorithms in both prediction and control settings.
A Generalized Bootstrap Target for Value-Learning, Efficiently Combining Value and Feature Predictions
We illustrate that incorporating predictive knowledge through an $\eta\gamma$-discounted SF model makes more efficient use of sampled experience, compared to either extreme, i. e. bootstrapping entirely on the value function estimate, or bootstrapping on the product of separately estimated successor features and instantaneous reward models.
Learning Expected Emphatic Traces for Deep RL
We develop a multi-step emphatic weighting that can be combined with replay, and a time-reversed $n$-step TD learning algorithm to learn the required emphatic weighting.
Forethought and Hindsight in Credit Assignment
We address the problem of credit assignment in reinforcement learning and explore fundamental questions regarding the way in which an agent can best use additional computation to propagate new information, by planning with internal models of the world to improve its predictions.
