Reward-Free Deep-Learning-Based Reinforcement Learning
Published in European Workshop on Reinforcement Learning, 2025
Exploration is widely regarded as one of the most challenging aspects of reinforcement learning (RL). We consider the reward-free RL problem, which operates in two phases: an exploration phase, where the agent gathers exploration trajectories over episodes irrespective of any predetermined reward function, and a subsequent planning phase, where a reward function is introduced. The agent then utilizes the episodes from the exploration phase to calculate a near-optimal policy. Existing algorithms and sample complexities for reward-free RL are limited to tabular, linear, or very smooth function approximations, leaving the problem largely open for more general cases. We consider deep-learning-based function approximations, i.e. DQNs, and propose an algorithm based on internal feedback and the agent’s own confidence and self-certainty in a graph MDP
Recommended citation: Shabbar, Aya. (2024). Reward-Free Deep-Learning-Based Reinforcement Learning
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