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Universal Successor Representations for Transfer Reinforcement Learning
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The objective of transfer reinforcement learning is to generalize from a set of previous tasks to unseen new tasks. In this work, we focus on the transfer scenario where the dynamics among tasks are the same, but their goals differ. Although general value function (Sutton et al., 2011) has been shown to be useful for knowledge transfer, learning a universal value function can be challenging in practice. To attack this, we propose (1) to use universal successor representations (USR) to represent the transferable knowledge and (2) a USR approximator (USRA) that can be trained by interacting with the environment. Our experiments show that USR can be effectively applied to new tasks, and the agent initialized by the trained USRA can achieve the goal considerably faster than random initialization.
Forward citations
Cited by 3 Pith papers
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Learning Action-Transferable Policy with Action Embedding
TRACE learns action embeddings from a transition model and transfers policies across reinforcement learning tasks with different state and action spaces, improving sample efficiency in experiments.
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VUSFA:Variational Universal Successor Features Approximator to Improve Transfer DRL for Target Driven Visual Navigation
VUSFA combines universal successor features, a successor-feature-dependent policy, and a variational information bottleneck to improve target-driven visual navigation transfer in the AI2THOR simulator.
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