Self-Supervised Learning for Reversible Robotics Actions
Reinforcement learning (RL) has become a cornerstone in training autonomous agents for domains ranging from robotics to chip layout optimization. While RL excels at discovering task solutions from scratch, it often lacks an intrinsic understanding of whether its actions can be undone. In many engineered systems, especially in robotics, this knowledge is critical. Mechanical wear, costly repairs, and safety concerns all demand that agents avoid actions that cause irreversible damage. Estimating reversibility requires an implicit grasp of the environment’s physics, yet standard RL agents typically operate without such a model.
A method presented at NeurIPS 2021, titled “There Is No Turning Back: A Self-Supervised Approach to Reversibility-Aware Reinforcement Learning,” addresses this gap. The approach augments the RL process with a dedicated reversibility estimation module, trained in a self-supervised manner from unlabeled interaction data. This module can be developed alongside the RL agent during training or from pre-collected datasets. Its purpose is to steer the policy toward reversible behaviors without requiring explicit labels for reversibility.
The core idea relies on a proxy measure called precedence — the probability that event A occurs before event B, given that both occur. This metric emerges naturally from interaction data and correlates with reversibility. For example, when a glass falls from a table and shatters, the sequence from table height to floor is always one-way; precedence probability is 1, signaling irreversibility. A rubber ball dropped from the same height, however, alternates between positions, yielding a precedence probability of 0.5, indicative of reversibility.
In practice, the method samples pairs of events from recorded trajectories, shuffles them, and trains a neural network to reconstruct their true chronological order. The network’s confidence in ordering serves as a reversibility indicator. Events with high-confidence precedence above a set threshold are deemed irreversible. Sampling is restricted to a fixed temporal window to avoid trivial or impossible orderings.
Two integration strategies emerge from this framework. Reversibility-Aware Exploration (RAE) modifies the reward function to penalize irreversible transitions, making them less likely but not forbidden. Reversibility-Aware Control (RAC) acts as a filter between policy and environment, rejecting irreversible actions outright and prompting the agent to select alternatives.
The distinction is practical: RAE suits scenarios where occasional irreversible actions are acceptable if they yield significant benefits, while RAC is better for safety-critical contexts where irreversibility must be avoided entirely.
Testing in a synthetic navigation environment illustrates RAE’s effect. An agent tasked with reaching a goal could either follow a designated path or cut across grass, leaving a permanent brown trail. A standard Proximal Policy Optimization (PPO) agent favored the shortest route, damaging the grass. A PPO agent augmented with RAE avoided the grass entirely, preserving the environment without explicit penalties.
In the classic Cartpole task, where irreversible actions cause the pole to fall, RAC proved decisive. With a maximum of 50,000 steps allowed, a random policy combined with RAC achieved the maximum score when the irreversibility threshold ? was set to 0.4. Standard model-free agents such as DQN and M-DQN typically scored under 3,000.
The Sokoban puzzle game provided a more complex test. Here, pushing a box against a wall can create a deadlock, as boxes cannot be pulled. Standard agents exploring randomly often became trapped early. An IMPALA agent equipped with RAE encountered fewer deadlocks and achieved higher scores across 1,000 levels. Notably, about half of Sokoban levels require at least one irreversible action to complete, often because target locations are adjacent to walls. RAE did not prevent these necessary moves, demonstrating its ability to balance caution with task completion.
This self-supervised reversibility estimation offers a scalable way to enhance RL agents’ safety and efficiency. By learning temporal ordering from interaction data, agents gain a probabilistic sense of which actions can be undone. The approach requires no prior reversibility annotations, making it adaptable to diverse environments, from delicate robotic manipulators to autonomous vehicles operating in dynamic settings.
