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EMOTE: An Explainable architecture for Modelling the Other Through Empathy
This involves learning an "Imagination Network" to transform the other agent's observed state in order to produce a human-interpretable "empathetic state" which, when presented to the learning agent, produces behaviours that mimic the other agent.
Learning to Constrain Policy Optimization with Virtual Trust Region
We introduce a constrained optimization method for policy gradient reinforcement learning, which uses a virtual trust region to regulate each policy update.
Model-Based Episodic Memory Induces Dynamic Hybrid Controls
Episodic control enables sample efficiency in reinforcement learning by recalling past experiences from an episodic memory.
Reachability Traces for Curriculum Design in Reinforcement Learning
The objective in goal-based reinforcement learning is to learn a policy to reach a particular goal state within the environment.
Neural Latent Traversal with Semantic Constraints
Whilst Generative Adversarial Networks (GANs) generate visually appealing high resolution images, the latent representations (or codes) of these models do not allow controllable changes on the semantic attributes of the generated images.
Plug and Play, Model-Based Reinforcement Learning
This is achieved by representing the global transition dynamics as a union of local transition functions, each with respect to one active object in the scene.
A New Representation of Successor Features for Transfer across Dissimilar Environments
Transfer in reinforcement learning is usually achieved through generalisation across tasks.
