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Found 10 papers, 2 papers with code
Uniqueness and Complexity of Inverse MDP Models
In the traditional "forward" view, transition "matrix" p(s'|sa) and policy {\pi}(a|s) uniquely determine "everything": the whole dynamics p(as'a's"a"...|s), and with it, the action-conditional state process p(s's"...|saa'a"), the multi-step inverse models p(aa'a"...|ss^i), etc.
Entropic Desired Dynamics for Intrinsic Control
An agent might be said, informally, to have mastery of its environment when it has maximised the effective number of states it can reliably reach.
Wasserstein Distance Maximizing Intrinsic Control
This paper deals with the problem of learning a skill-conditioned policy that acts meaningfully in the absence of a reward signal.
Learning more skills through optimistic exploration
However, an inherent exploration problem lingers: when a novel state is actually encountered, the discriminator will necessarily not have seen enough training data to produce accurate and confident skill classifications, leading to low intrinsic reward for the agent and effective penalization of the sort of exploration needed to actually maximize the objective.
Beyond Fine-Tuning: Transferring Behavior in Reinforcement Learning
We introduce Behavior Transfer (BT), a technique that leverages pre-trained policies for exploration and that is complementary to transferring neural network weights.
Relative Variational Intrinsic Control
In the absence of external rewards, agents can still learn useful behaviors by identifying and mastering a set of diverse skills within their environment.
Generalization of Reinforcement Learners with Working and Episodic Memory
In this paper, we aim to develop a comprehensive methodology to test different kinds of memory in an agent and assess how well the agent can apply what it learns in training to a holdout set that differs from the training set along dimensions that we suggest are relevant for evaluating memory-specific generalization.
Fast Task Inference with Variational Intrinsic Successor Features
It has been established that diverse behaviors spanning the controllable subspace of an Markov decision process can be trained by rewarding a policy for being distinguishable from other policies \citep{gregor2016variational, eysenbach2018diversity, warde2018unsupervised}.
Unsupervised Control Through Non-Parametric Discriminative Rewards
Learning to control an environment without hand-crafted rewards or expert data remains challenging and is at the frontier of reinforcement learning research.
Fast deep reinforcement learning using online adjustments from the past
We propose Ephemeral Value Adjusments (EVA): a means of allowing deep reinforcement learning agents to rapidly adapt to experience in their replay buffer.
