Universal Successor Features Approximators

Abstract

The ability of a reinforcement learning (RL) agent to learn about many reward functions at the same time has many potential benefits, such as the decomposition of complex tasks into simpler ones, the exchange of information between tasks, and the reuse of skills. We focus on one aspect in particular, namely the ability to generalise to unseen tasks. Parametric generalisation relies on the interpolation power of a function approximator that is given the task description as input; one of its most common form are universal value function approximators (UVFAs). Another way to generalise to new tasks is to exploit structure in the RL problem itself. Generalised policy improvement (GPI) combines solutions of previous tasks into a policy for the unseen task; this relies on instantaneous policy evaluation of old policies under the new reward function, which is made possible through successor features (SFs). Our proposed universal successor features approximators (USFAs) combine the advantages

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyborsa2018universal

Related papers

• Universal Successor Representations For Transfer Reinforcement Learning (2018)0.00
• Successor Feature Sets: Generalizing Successor Representations Across Policies (2021)5.84
• Successor Feature Representations (2021)0.00
• Successor Features Combine Elements Of Model-free And Model-based Reinforcement Learning (2019)0.00
• Advantages And Limitations Of Using Successor Features For Transfer In Reinforcement Learning (2017)0.00
• Successor Features For Transfer In Reinforcement Learning (2016)0.00
• SF-DQN: Provable Knowledge Transfer Using Successor Feature For Deep Reinforcement Learning (2024)0.00
• Fast Task Inference With Variational Intrinsic Successor Features (2019)0.00