A major challenge in real-world reinforcement learning (RL) is the sparsity of reward feedback. Often, what is available is an intuitive but sparse reward function that only indicates whether the task is completed partially or fully. However, the lack of carefully designed, fine grain feedback implies that most existing RL algorithms fail to learn an acceptable policy in a reasonable time frame. This is because of the large number of exploration actions that the policy has to perform before it gets any useful feedback that it can learn from. In this work, we address this challenging problem by developing an algorithm that exploits the offline demonstration data generated by a sub-optimal behavior policy for faster and efficient online RL in such sparse reward settings. The proposed algorithm, which we call the Learning Online with Guidance Offline (LOGO) algorithm, merges a policy improvement step with an additional policy guidance step by using the offline demonstration data. The key idea is that by obtaining guidance from - not imitating - the offline data, LOGO orients its policy in the manner of the sub-optimal policy, while yet being able to learn beyond and approach optimality. We provide a theoretical analysis of our algorithm, and provide a lower bound on the performance improvement in each learning episode. We also extend our algorithm to the even more challenging incomplete observation setting, where the demonstration data contains only a censored version of the true state observation. We demonstrate the superior performance of our algorithm over state-of-the-art approaches on a number of benchmark environments with sparse rewards and censored state. Further, we demonstrate the value of our approach via implementing LOGO on a mobile robot for trajectory tracking and obstacle avoidance, where it shows excellent performance.
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Enhanced Meta Reinforcement Learning using Demonstrations in Sparse Reward Environments
Meta reinforcement learning (Meta-RL) is an approach wherein the experience
gained from solving a variety of tasks is distilled into a meta-policy. The metapolicy,
when adapted over only a small (or just a single) number of steps, is able
to perform near-optimally on a new, related task. However, a major challenge to
adopting this approach to solve real-world problems is that they are often associated
with sparse reward functions that only indicate whether a task is completed partially
or fully. We consider the situation where some data, possibly generated by a suboptimal
agent, is available for each task. We then develop a class of algorithms
entitled Enhanced Meta-RL using Demonstrations (EMRLD) that exploit this
information—even if sub-optimal—to obtain guidance during training. We show
how EMRLD jointly utilizes RL and supervised learning over the offline data to
generate a meta-policy that demonstrates monotone performance improvements.
We also develop a warm started variant called EMRLD-WS that is particularly
efficient for sub-optimal demonstration data. Finally, we show that our EMRLD
algorithms significantly outperform existing approaches in a variety of sparse
reward environments, including that of a mobile robot.
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- Award ID(s):
- 1955696
- PAR ID:
- 10472691
- Publisher / Repository:
- Curran Associates
- Date Published:
- Journal Name:
- Advances in neural information processing systems
- ISSN:
- 1049-5258
- Format(s):
- Medium: X
- Location:
- New Orleans, LA
- Sponsoring Org:
- National Science Foundation
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Abstract The meta-reinforcement learning (meta-RL) framework, which involves RL over multiple timescales, has been successful in training deep RL models that generalize to new environments. It has been hypothesized that the prefrontal cortex may mediate meta-RL in the brain, but the evidence is scarce. Here we show that the orbitofrontal cortex (OFC) mediates meta-RL. We trained mice and deep RL models on a probabilistic reversal learning task across sessions during which they improved their trial-by-trial RL policy through meta-learning. Ca2+/calmodulin-dependent protein kinase II-dependent synaptic plasticity in OFC was necessary for this meta-learning but not for the within-session trial-by-trial RL in experts. After meta-learning, OFC activity robustly encoded value signals, and OFC inactivation impaired the RL behaviors. Longitudinal tracking of OFC activity revealed that meta-learning gradually shapes population value coding to guide the ongoing behavioral policy. Our results indicate that two distinct RL algorithms with distinct neural mechanisms and timescales coexist in OFC to support adaptive decision-making.
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Accepted Manuscript1.0
- Conference Proceeding:
- The DOI is not currently available.
