Even if the optimal policy is obtained precisely, it is often the case the optimal policy is deterministic.

This work addresses the problem of offline safe imitation learning (IL), where the goal is to learn safe and reward-maximizing policies from demonstrations that do not have per-timestep safety cost or reward information. In many real-world domains, online learning in the environment can be risky, and specifying accurate safety costs can be difficult. However, it is often feasible to collect trajectories that reflect undesirable or unsafe behavior, implicitly conveying what the agent should avoid. We refer to these as non-preferred trajectories. We propose a novel offline safe IL algorithm, OSIL, that infers safety from non-preferred demonstrations. We formulate safe policy learning as a Constrained Markov Decision Process (CMDP). Instead of relying on explicit safety cost and reward annotations, OSIL reformulates the CMDP problem by deriving a lower bound on reward maximizing objective and learning a cost model that estimates the likelihood of non-preferred behavior. Our approach allows agents to learn safe and reward-maximizing behavior entirely from offline demonstrations. We empirically demonstrate that our approach can learn safer policies that satisfy cost constraints without degrading the reward performance, thus outperforming several baselines.

Knowing how to efficiently balance between exploring unfamiliar parts of an environment and exploiting currently available knowledge is an essential ingredient of anyintelligent organism.

In this paper, we consider the setting where the learner has its own preferences that it additionally takesintoconsideration.

The24], which Q-learning exploration Q-function Q-function asymptotically 39] derived drawbackof example, Zou39] require lowerbounded properties.

Ourapproach, Exploration through Learned Language Abstraction (ELLA) providesintermediate rewards to an agent for completing relevant low-level behaviors as it tries to solve a complex, sparse rewardtask.

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