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A Conceptual Framework for Externally-influenced Agents: An Assisted Reinforcement Learning Review
In this work, while reviewing externally-influenced methods, we propose a conceptual framework and taxonomy for assisted reinforcement learning, aimed at fostering collaboration by classifying and comparing various methods that use external information in the learning process.
Directed Policy Gradient for Safe Reinforcement Learning with Human Advice
Our technique, Directed Policy Gradient (DPG), allows a teacher or backup policy to override the agent before it acts undesirably, while allowing the agent to leverage human advice or directives to learn faster.
Using PCA to Efficiently Represent State Spaces
When using dimensionality reduction in Mario, learning converges much faster to a good policy.
Off-Policy Reward Shaping with Ensembles
While PBRS is proven to always preserve optimal policies, its effect on learning speed is determined by the quality of its potential function, which, in turn, depends on both the underlying heuristic and the scale.
Off-Policy Shaping Ensembles in Reinforcement Learning
Recent advances of gradient temporal-difference methods allow to learn off-policy multiple value functions in parallel with- out sacrificing convergence guarantees or computational efficiency.
