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Found 4 papers, 1 papers with code
Predictive Control Using Learned State Space Models via Rolling Horizon Evolution
A large part of the interest in model-based reinforcement learning derives from the potential utility to acquire a forward model capable of strategic long term decision making.
Generalising Discrete Action Spaces with Conditional Action Trees
There are relatively few conventions followed in reinforcement learning (RL) environments to structure the action spaces.
Modulation of viability signals for self-regulatory control
In particular, we highlight the distinction between observations induced by the environment and those pertaining more directly to the continuity of an agent in time.
Bootstrapped model learning and error correction for planning with uncertainty in model-based RL
Having access to a forward model enables the use of planning algorithms such as Monte Carlo Tree Search and Rolling Horizon Evolution.
