Learning to Mix n-Step Returns: Generalizing lambda-Returns for Deep Reinforcement Learning

TL;DR

This paper introduces Confidence-based Autodidactic Returns (CAR), a novel method for learning optimal n-step return weights in Deep Reinforcement Learning, outperforming traditional lambda-returns in Atari 2600 benchmarks.

Contribution

It provides an exhaustive benchmark of lambda-returns and proposes CAR, enabling RL agents to learn return weighting end-to-end, moving beyond fixed exponential decay schemes.

Findings

CAR outperforms lambda-returns and fixed n-step returns in Atari 2600 experiments.

End-to-end learned weighting schemes improve RL performance.

Benchmark results highlight the advantages of adaptive return weighting.

Abstract

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.