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A State Representation for Diminishing Rewards

Here we present GIFs of agents trained using the λR across several navigation domains. A consistent theme is that accurate policy evaluation using the ``correct’’ λ leads to stronger performance. Using a λ which is too high typically leads to overstaying at reward locations, while a λ which is too low leads the agent to abandon rewarded locations too early.

Policy Learning in TwoRooms

In this this setting the true λ is 0.5.

Agent λ = 0.5:

Agent λ = 0.0:

Agent λ = 1.0:

Policy Composition in FourRooms

Agent λ = 0.5:

Agent λ = 0.0:

Agent λ = 1.0:

Pixel-based agent:

Replenishing Rewards in TwoRooms