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Exploiting Causal Graph Priors with Posterior Sampling for Reinforcement Learning
The prior is typically specified as a class of parametric distributions, the design of which can be cumbersome in practice, often resulting in the choice of uninformative priors.
Provably Efficient Causal Model-Based Reinforcement Learning for Systematic Generalization
In this setting, the agent can take a finite amount of reward-free interactions from a subset of these environments.
The Importance of Non-Markovianity in Maximum State Entropy Exploration
In the maximum state entropy exploration framework, an agent interacts with a reward-free environment to learn a policy that maximizes the entropy of the expected state visitations it is inducing.
Challenging Common Assumptions in Convex Reinforcement Learning
In particular, we show that erroneously optimizing the infinite trials objective in place of the actual finite trials one, as it is usually done, can lead to a significant approximation error.
