Hindsight Goal Ranking on Replay Buffer for Sparse Reward Environment
This is an incremental improvement for robotic manipulation tasks, addressing sample inefficiency in sparse reward environments.
The paper tackles the problem of sparse rewards in reinforcement learning by proposing Hindsight Goal Ranking (HGR), a method that prioritizes replay experiences based on temporal difference error to accelerate learning, achieving significantly faster training than non-prioritized methods on four robotic manipulation tasks.
This paper proposes a method for prioritizing the replay experience referred to as Hindsight Goal Ranking (HGR) in overcoming the limitation of Hindsight Experience Replay (HER) that generates hindsight goals based on uniform sampling. HGR samples with higher probability on the states visited in an episode with larger temporal difference (TD) error, which is considered as a proxy measure of the amount which the RL agent can learn from an experience. The actual sampling for large TD error is performed in two steps: first, an episode is sampled from the relay buffer according to the average TD error of its experiences, and then, for the sampled episode, the hindsight goal leading to larger TD error is sampled with higher probability from future visited states. The proposed method combined with Deep Deterministic Policy Gradient (DDPG), an off-policy model-free actor-critic algorithm, accelerates learning significantly faster than that without any prioritization on four challenging simulated robotic manipulation tasks. The empirical results show that HGR uses samples more efficiently than previous methods across all tasks.