Core Concepts
A new algorithm, Reset Free RL with Intelligently Switching Controller (RISC), intelligently switches between a forward controller that tries to accomplish the task and a reset controller that tries to recover the initial state distribution, based on the agent's confidence in achieving its current goal. This allows RISC to maximize the experience generation in areas of the state-goal space that the agent still needs to learn more from.
Abstract
The paper explores how to improve performance in reset-free reinforcement learning (RL) by switching between controllers more intelligently. In reset-free RL, the strong episode resetting mechanisms needed to train agents in simulation are unavailable, which limits the potential of RL in the real world.
The key insights are:
- Proper bootstrapping when switching controllers is crucial for reset-free RL agents to maintain consistent learning targets and improve performance.
- Learning when to switch controllers, rather than using a fixed time limit, can help the agent learn more efficiently by gathering experience for states and goals it has not mastered yet.
The authors propose Reset Free RL with Intelligently Switching Controller (RISC), which:
- Uses timeout-nonterminal bootstrapping to maintain consistent learning targets when switching controllers.
- Learns a "success critic" to estimate the agent's competency in achieving its current goal, and switches controllers probabilistically based on this.
- Modulates the switching behavior to avoid excessive switching.
RISC achieves state-of-the-art performance on several challenging reset-free RL environments from the EARL benchmark, outperforming prior methods like Forward-Backward RL, R3L, VapRL, and MEDAL. The authors also show that a reverse curriculum, a common approach in prior work, may not be the best strategy for reset-free RL.
Stats
The paper does not provide any specific numerical data or statistics to support the key claims. The results are presented in the form of performance comparisons on benchmark environments.
Quotes
"Crucially, the part of these algorithms that switches between the controllers has gone understudied."
"Bootstrapping is when the agent updates some value estimate using an existing value estimate of a successor state (Sutton & Barto, 2018). It is a common idea used across many RL algorithms."
"Because reset-free RL is a unique setting in that there is no episode time limit imposed by the environment while training, the duration of the agent's controllers' trajectories becomes a parameter of the solution method."