Tag Archives: Sample Efficiency

Improving sample efficiency under sparse rewards and large continuous action spaces through predictive control in RL

October 10, 2024 09:26 ,

Antonyshyn, L., Givigi, S., Deep Model-Based Reinforcement Learning for Predictive Control of Robotic Systems with Dense and Sparse Rewards, J Intell Robot Syst 110, 100 (2024) DOI: 10.1007/s10846-024-02118-y.

Sparse rewards and sample efficiency are open areas of research in the field of reinforcement learning. These problems are especially important when considering applications of reinforcement learning to robotics and other cyber-physical systems. This is so because in these domains many tasks are goal-based and naturally expressed with binary successes and failures, action spaces are large and continuous, and real interactions with the environment are limited. In this work, we propose Deep Value-and-Predictive-Model Control (DVPMC), a model-based predictive reinforcement learning algorithm for continuous control that uses system identification, value function approximation and sampling-based optimization to select actions. The algorithm is evaluated on a dense reward and a sparse reward task. We show that it can match the performance of a predictive control approach to the dense reward problem, and outperforms model-free and model-based learning algorithms on the sparse reward task on the metrics of sample efficiency and performance. We verify the performance of an agent trained in simulation using DVPMC on a real robot playing the reach-avoid game. Video of the experiment can be found here: https://youtu.be/0Q274kcfn4c.

Posted in: Reinforcement learning in AI , Tagged: , , ,

Reducing the need of samples in RL through evolutionary techniques

October 10, 2024 09:22 ,

Onori, G., Shahid, A.A., Braghin, F. et al. , Adaptive Optimization of Hyper-Parameters for Robotic Manipulation through Evolutionary Reinforcement Learning, J Intell Robot Syst 110, 108 (2024) DOI: 10.1007/s10846-024-02138-8.

Deep Reinforcement Learning applications are growing due to their capability of teaching the agent any task autonomously and generalizing the learning. However, this comes at the cost of a large number of samples and interactions with the environment. Moreover, the robustness of learned policies is usually achieved by a tedious tuning of hyper-parameters and reward functions. In order to address this issue, this paper proposes an evolutionary RL algorithm for the adaptive optimization of hyper-parameters. The policy is trained using an on-policy algorithm, Proximal Policy Optimization (PPO), coupled with an evolutionary algorithm. The achieved results demonstrate an improvement in the sample efficiency of the RL training on a robotic grasping task. In particular, the learning is improved with respect to the baseline case of a non-evolutionary agent. The evolutionary agent needs % fewer samples to completely learn the grasping task, enabled by the adaptive transfer of knowledge between the agents through the evolutionary algorithm. The proposed approach also demonstrates the possibility of updating reward parameters during training, potentially providing a general approach to creating reward functions.

Posted in: Applications of reinforcement learning to robots , Tagged: , ,

A relatively simple way of reducing the sampling cost of DQN

September 19, 2024 08:12 ,

Hossein Hassani, Soodeh Nikan, Abdallah Shami, Traffic navigation via reinforcement learning with episodic-guided prioritized experience replay, Engineering Applications of Artificial Intelligence, Volume 137, Part A, 2024, DOI: 10.1016/j.engappai.2024.109147.

Deep Reinforcement Learning (DRL) models play a fundamental role in autonomous driving applications; however, they typically suffer from sample inefficiency because they often require many interactions with the environment to learn effective policies. This makes the training process time-consuming. To address this shortcoming, Prioritized Experience Replay (PER) has proven to be effective by prioritizing samples with high Temporal-Difference (TD) error for learning. In this context, this study contributes to artificial intelligence by proposing a sample-efficient DRL algorithm called Episodic-Guided Prioritized Experience Replay (EPER). The core innovation of EPER lies in the utilization of an episodic memory, dedicated to storing successful training episodes. Within this memory, expected returns for each state–action pair are extracted. These returns, combined with TD error-based prioritization, form a novel objective function for deep Q-network training. To prevent excessive determinism, EPER introduces exploration into the learning process by incorporating a regularization term into the objective function that allows exploration of state-space regions with diverse Q-values. The proposed EPER algorithm is suitable to train a DRL agent for handling episodic tasks, and it can be integrated into off-policy DRL models. EPER is employed for traffic navigation through scenarios such as highway driving, merging, roundabout, and intersection to showcase its application in engineering. The attained results denote that, compared with the PER and an additional state-of-the-art training technique, EPER is superior in expediting the training of the agent and learning a more optimal policy that leads to lower collision rates within the constructed navigation scenarios.

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Improving sample efficiency of RL through memory reconstruction

February 15, 2024 09:29 ,

Y. Kang et al., Sample Efficient Reinforcement Learning Using Graph-Based Memory Reconstruction, IEEE Transactions on Artificial Intelligence, vol. 5, no. 2, pp. 751-762, Feb. 2024 DOI: 10.1109/TAI.2023.3268612.

Reinforcement learning (RL) algorithms typically require orders of magnitude more interactions than humans to learn effective policies. Research on memory in neuroscience suggests that humans’ learning efficiency benefits from associating their experiences and reconstructing potential events. Inspired by this finding, we introduce a human brainlike memory structure for agents and build a general learning framework based on this structure to improve the RL sampling efficiency. Since this framework is similar to the memory reconstruction process in psychology, we name the newly proposed RL framework as graph-based memory reconstruction (GBMR). In particular, GBMR first maintains an attribute graph on the agent’s memory and then retrieves its critical nodes to build and update potential paths among these nodes. This novel pipeline drives the RL agent to learn faster with its memory-enhanced value functions and reduces interactions with the environment by reconstructing its valuable paths. Extensive experimental analyses and evaluations in the grid maze and some challenging Atari environments demonstrate GBMRs superiority over traditional RL methods. We will release the source code and trained models to facilitate further studies in this research direction.

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