lehnert2017advantages: Advantages and Limitations of using Successor Features for Transfer in Reinforcement Learning
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source :
- authors
- Lehnert, L., Tellex, S., & Littman, M. L.
- year
- 2017
This paper looks at how well SFs transfer on some controlled problems. They found that Fitted SF often comes to a good solution, but has several limitations:
- When the reward function is sufficiently different from the previous reward functions, the SFs are unable to learn an optimal policy. This is possibly because the SFs are conditioned on the initial reward function seen by the agent.
- SF takes longer to learn the initial problem than fitted q-learning. This is likely to do w/ SFs needing to learn a full reward model rather than just a value function.
The conclusion is the representation could be an area of exploration for transfer in RL, but SFs are not as generally useful as one would hope as they are tied to the optimal policy of the first rewarding function.