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We propose an approach for model-driven generation of RL environment simulations with discrete action spaces using goal models. The proposal utilizes earlier ...
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Oct 12, 2024 · In this paper, we use model-driven engineering (MDE) methods and tools for developing a domain-specific modelling environment to contribute towards providing a ...
The pre-trained goal prior model acts as a regularizer for the high-level policy during RL, providing intrinsic rewards that guide the agent's exploration ...
Oct 17, 2024 · In this paper, we use model-driven engineering (MDE) methods and tools for developing a domain-specific modelling environment to contribute ...
One of the gnarliest challenges in reinforcement learning (RL) is exploration that scales to vast do- mains, where novelty-, or coverage-seeking be-.
Oct 26, 2020 · Our results show that a curriculum of co-evolving the en- vironment difficulty together with the difficulty of goals set in each environment.
Reverse Curriculum Generation for Reinforcement Learning Agents
bair.berkeley.edu › blog › 2017/12/20
Dec 20, 2017 · In goal-oriented tasks the aim is to reach a desired configuration from any start state. For example, in the ring-on-peg task introduced above, ...
Mar 9, 2022 · This work focuses on the adaptability of two explainability methods in continuous environments. The methods based on learning and introspection ...
This work proposes a reinforcement learning-based computational design space exploration methodology to generate optimal in-silico protocols for the simulated ...
Abstract. Most approaches for goal recognition rely on specifications of the possible dynamics of the actor in the environment when pursuing a goal.