  Reinforcement Learning (RL) can model complex behavior policies for
goal-directed sequential decision making tasks. A hallmark of RL algorithms is
Temporal Difference (TD) learning: value function for the current state is
moved towards a bootstrapped target that is estimated using next state's value
function. $\lambda$-returns generalize beyond 1-step returns and strike a
balance between Monte Carlo and TD learning methods. While lambda-returns have
been extensively studied in RL, they haven't been explored a lot in Deep RL.
This paper's first contribution is an exhaustive benchmarking of
lambda-returns. Although mathematically tractable, the use of exponentially
decaying weighting of n-step returns based targets in lambda-returns is a
rather ad-hoc design choice. Our second major contribution is that we propose a
generalization of lambda-returns called Confidence-based Autodidactic Returns
(CAR), wherein the RL agent learns the weighting of the n-step returns in an
end-to-end manner. This allows the agent to learn to decide how much it wants
to weigh the n-step returns based targets. In contrast, lambda-returns restrict
RL agents to use an exponentially decaying weighting scheme. Autodidactic
returns can be used for improving any RL algorithm which uses TD learning. We
empirically demonstrate that using sophisticated weighted mixtures of
multi-step returns (like CAR and lambda-returns) considerably outperforms the
use of n-step returns. We perform our experiments on the Asynchronous Advantage
Actor Critic (A3C) algorithm in the Atari 2600 domain.
