MULEX: Disentangling Exploitation from Exploration in Deep RL
This addresses a fundamental challenge in RL for agents learning through interactions, though it appears incremental as it builds on existing off-policy methods.
The paper tackles the exploration-exploitation trade-off in deep reinforcement learning by explicitly disentangling exploration and exploitation through separate losses optimized in parallel, and demonstrates sample-efficiency and robustness on a hard-exploration environment.
An agent learning through interactions should balance its action selection process between probing the environment to discover new rewards and using the information acquired in the past to adopt useful behaviour. This trade-off is usually obtained by perturbing either the agent's actions (e.g., e-greedy or Gibbs sampling) or the agent's parameters (e.g., NoisyNet), or by modifying the reward it receives (e.g., exploration bonus, intrinsic motivation, or hand-shaped rewards). Here, we adopt a disruptive but simple and generic perspective, where we explicitly disentangle exploration and exploitation. Different losses are optimized in parallel, one of them coming from the true objective (maximizing cumulative rewards from the environment) and others being related to exploration. Every loss is used in turn to learn a policy that generates transitions, all shared in a single replay buffer. Off-policy methods are then applied to these transitions to optimize each loss. We showcase our approach on a hard-exploration environment, show its sample-efficiency and robustness, and discuss further implications.