Public articles linked to the same research event.
arXiv The work presents Wayfarer, a general, domain-agnostic, online deep RL agent that discovers options through Laplacian representation learning from high-dimensional observations and leverages them for control; the authors report that the resulting options simultaneously improve exploration, accelerate credit assignment, and generalise effectively to unseen settings, enabling substantially faster learning of complex policies and achieving state-of-the-art performance among single-stream agents on the most challenging Atari 2600 games, with the largest gains in games requiring long-horizon exploration and strategic behaviour, such as Montezuma's Revenge and Private Eye.
The work presents Wayfarer, a general, domain-agnostic, online deep RL agent that discovers options through Laplacian representation learning from high-dimensional observations and leverages them for control; the authors report that the resulting options simultaneously improve exploration, accelerate credit assignment, and generalise effectively to unseen settings, enabling substantially faster learning of complex policies and achieving state-of-the-art performance among single-stream agents on the most challenging Atari 2600 games, with the largest gains in games requiring long-horizon exploration and strategic behaviour, such as Montezuma's Revenge and Private Eye.
The work presents Wayfarer, a general, domain-agnostic, online deep RL agent that discovers options through Laplacian representation learning from high-dimensional observations and leverages them for control; the authors report that the resulting options simultaneously improve exploration, accelerate credit assignment, and generalise effectively to unseen settings, enabling substantially faster learning of complex policies and achieving state-of-the-art performance among single-stream agents on the most challenging Atari 2600 games, with the largest gains in games requiring long-horizon exploration and strategic behaviour, such as Montezuma's Revenge and Private Eye.
The work presents Wayfarer, a general, domain-agnostic, online deep RL agent that discovers options through Laplacian representation learning from high-dimensional observations and leverages them for control; the authors report that the resulting options simultaneously improve exploration, accelerate credit assignment, and generalise effectively to unseen settings, enabling substantially faster learning of complex policies and achieving state-of-the-art performance among single-stream agents on the most challenging Atari 2600 games, with the largest gains in games requiring long-horizon exploration and strategic behaviour, such as Montezuma's Revenge and Private Eye.