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Found 8 papers, 3 papers with code
Lazy-MDPs: Towards Interpretable Reinforcement Learning by Learning When to Act
We deem those states and corresponding actions important since they explain the difference in performance between the default and the new, lazy policy.
More Efficient Exploration with Symbolic Priors on Action Sequence Equivalences
Incorporating prior knowledge in reinforcement learning algorithms is mainly an open question.
Better state exploration using action sequence equivalence
Incorporating prior knowledge in reinforcement learning algorithms is mainly an open question.
There Is No Turning Back: A Self-Supervised Approach for Reversibility-Aware Reinforcement Learning
We propose to learn to distinguish reversible from irreversible actions for better informed decision-making in Reinforcement Learning (RL).
Adversarially Guided Actor-Critic
Despite definite success in deep reinforcement learning problems, actor-critic algorithms are still confronted with sample inefficiency in complex environments, particularly in tasks where efficient exploration is a bottleneck.
Self-Imitation Advantage Learning
Self-imitation learning is a Reinforcement Learning (RL) method that encourages actions whose returns were higher than expected, which helps in hard exploration and sparse reward problems.
Acme: A Research Framework for Distributed Reinforcement Learning
These implementations serve both as a validation of our design decisions as well as an important contribution to reproducibility in RL research.
Self-Attentional Credit Assignment for Transfer in Reinforcement Learning
The ability to transfer knowledge to novel environments and tasks is a sensible desiderata for general learning agents.
