מושגי ליבה
Proposing a waypoint-based approach for model-free reinforcement learning in robot manipulation tasks, leading to faster task learning and improved performance.
תקציר
The content discusses the proposal of a waypoint-based approach for model-free reinforcement learning in robot manipulation tasks. It introduces a method that builds trajectories one waypoint at a time, leveraging multi-armed bandits. The theoretical implications and lower bounds on regret are explored, along with an algorithm based on posterior sampling. Benchmark simulations and real-world experiments demonstrate the effectiveness of the proposed approach over state-of-the-art baselines.
I. Introduction
- Reinforcement learning framework for robot arms.
- Existing approaches and challenges in reinforcement learning.
- Proposal of waypoint-based approach for model-free reinforcement learning.
II. Waypoint-Based Approach
- Formulating as Sequential Multi-Armed Bandits.
- Learning waypoints via posterior sampling.
- Testing in simulated and real environments.
III. Problem Formulation
- Settings where robot arm learns reward function.
- Model-free reinforcement learning approaches.
- Regret analysis and lower bounds.
IV. Reinforcement Learning with Sequential Waypoints
- Reformulation as a sequence of multi-armed bandits.
- Assumptions behind the formulation.
- Lower bounds on regret and theoretical implications.
- Approximate solution with posterior sampling algorithm.
V. Benchmark Simulations
- Evaluation across simulated benchmark tasks.
- Comparison with state-of-the-art baselines (SAC, PPO).
- Performance results and rewards achieved by each method.
VI. Real-world Experiments
- Evaluation of proposed approach (Ours) against SAC in real-world tasks.
- Experimental setup, training procedures, and results obtained.
סטטיסטיקה
Each interaction lasts H = 100 timesteps across all environments.
ציטוטים
"Many manipulation tasks can be broken down into high-level waypoints."
"Our proposed approach outperformed common model-free reinforcement learning algorithms."