Proxy Experience Replay: Federated Distillation for Distributed Reinforcement Learning

Enhanced Experience Replay for Deep Reinforcement Learning

Autonomous reinforcement learning with experience replay.

This paper considers the issues of efficiency and autonomy that are required to make reinforcement learning suitable for real-life control tasks. A real-time reinforcement learning algorithm is presented that repeatedly adjusts the control policy with the use of previously collected samples, and autonomously estimates the appropriate step-sizes for the learning updates. The algorithm is based o...

Autonomous Reinforcement Learning with Experience Replay for Humanoid Gait Optimization

This paper demonstrates application of Reinforcement Learning to optimization of control of a complex system in realistic setting that requires efficiency and autonomy of the learning algorithm. Namely, Actor-Critic with experience replay (which addresses efficiency), and the Fixed Point method for step-size estimation (which addresses autonomy) is applied here to approximately optimize humanoi...

Truncated Importance Sampling for Reinforcement Learning with Experience Replay

Reinforcement Learning (RL) is considered here as an adaptation technique of neural controllers of machines. The goal is to make Actor-Critic algorithms require less agent-environment interaction to obtain policies of the same quality, at the cost of additional background computations. We propose to achieve this goal in the spirit of experience replay. An estimation method of improvement direct...

Knowledge Transfer for Deep Reinforcement Learning with Hierarchical Experience Replay

The process for transferring knowledge of multiple reinforcement learning policies into a single multi-task policy via distillation technique is known as policy distillation. When policy distillation is under a deep reinforcement learning setting, due to the giant parameter size and the huge state space for each task domain, it requires extensive computational efforts to train the multi-task po...