Bigger, Regularized, Optimistic: scaling for compute and sample-efficient continuous control

Sample efficiency in Reinforcement Learning (RL) has traditionally been driven by algorithmic enhancements. In this work, we demonstrate that scaling can also lead to substantial improvements. We conduct a thorough investigation into the interplay of scaling model capacity and domain-specific RL enhancements. These empirical findings inform the design choices underlying our proposed BRO (Bigger, Regularized, Optimistic) algorithm. The key innovation behind BRO is that strong regularization allows for effective scaling of the critic networks, which, paired with optimistic exploration, leads to superior performance. BRO achieves state-of-the-art results, significantly outperforming the leading model-based and model-free algorithms across 40 complex tasks from the DeepMind Control, MetaWorld, and MyoSuite benchmarks. BRO is the first model-free algorithm to achieve near-optimal policies in the notoriously challenging Dog and Humanoid tasks.

A Case for Validation Buffer in Pessimistic Actor-Critic

In this paper, we investigate the issue of error accumulation in critic networks updated via pessimistic temporal difference objectives. We show that the critic approximation error can be approximated via a recursive fixed-point model similar to that of the Bellman value. We use such recursive definition to retrieve the conditions under which the pessimistic critic is unbiased. Building on these insights, we propose Validation Pessimism Learning (VPL) algorithm. VPL uses a small validation buffer to adjust the levels of pessimism throughout the agent training, with the pessimism set such that the approximation error of the critic targets is minimized. We investigate the proposed approach on a variety of locomotion and manipulation tasks and report improvements in sample efficiency and performance.

Overestimation, Overfitting, and Plasticity in Actor-Critic: the Bitter Lesson of Reinforcement Learning

Recent advancements in off-policy Reinforcement Learning (RL) have significantly improved sample efficiency, primarily due to the incorporation of various forms of regularization that enable more gradient update steps than traditional agents. However, many of these techniques have been tested in limited settings, often on tasks from single simulation benchmarks and against well-known algorithms rather than a range of regularization approaches. This limits our understanding of the specific mechanisms driving RL improvements. To address this, we implemented over 60 different off-policy agents, each integrating established regularization techniques from recent state-of-the-art algorithms. We tested these agents across 14 diverse tasks from 2 simulation benchmarks. Our findings reveal that while the effectiveness of a specific regularization setup varies with the task, certain combinations consistently demonstrate robust and superior performance. Notably, a simple Soft Actor-Critic agent, appropriately regularized, reliably solves dog tasks, which were previously solved mainly through model-based approaches.

Decoupled Actor-Critic

Actor-Critic methods are in a stalemate of two seemingly irreconcilable problems. Firstly, critic proneness towards overestimation requires sampling temporal-difference targets from a conservative policy optimized using lower-bound Q-values. Secondly, well-known results show that policies that are optimistic in the face of uncertainty yield lower regret levels. To remedy this dichotomy, we propose Decoupled Actor-Critic (DAC). DAC is an off-policy algorithm that learns two distinct actors by gradient backpropagation: a conservative actor used for temporal-difference learning and an optimistic actor used for exploration. We test DAC on DeepMind Control tasks in low and high replay ratio regimes and ablate multiple design choices. Despite minimal computational overhead, DAC achieves state-of-the-art performance and sample efficiency on locomotion tasks.

On All-Action Policy Gradients

In this paper, we analyze the variance of stochastic policy gradient with many action samples per state (all-action SPG). We decompose the variance of SPG and derive an optimality condition for all-action SPG. The optimality condition shows when all-action SPG should be preferred over single-action counterpart and allows to determine a variance-minimizing sampling scheme in SPG estimation. Furthermore, we propose dynamics-all-action (DAA) module, an augmentation that allows for all-action sampling without manipulation of the environment. DAA addresses the problems associated with using a Q-network for all-action sampling and can be readily applied to any on-policy SPG algorithm. We find that using DAA with a canonical on-policy algorithm (PPO) yields better sample efficiency and higher policy returns on a variety of challenging continuous action environments.

Low-Variance Policy Gradient Estimation with World Models

In this paper, we propose World Model Policy Gradient (WMPG), an approach to reduce the variance of policy gradient estimates using learned world models (WM's). In WMPG, a WM is trained online and used to imagine trajectories. The imagined trajectories are used in two ways. Firstly, to calculate a without-replacement estimator of the policy gradient. Secondly, the return of the imagined trajectories is used as an informed baseline. We compare the proposed approach with AC and MAC on a set of environments of increasing complexity (CartPole, LunarLander and Pong) and find that WMPG has better sample efficiency. Based on these results, we conclude that WMPG can yield increased sample efficiency in cases where a robust latent representation of the environment can be learned.