Reverse Curriculum Generation for Reinforcement Learning (1707.05300v3)

Abstract: Many relevant tasks require an agent to reach a certain state, or to manipulate objects into a desired configuration. For example, we might want a robot to align and assemble a gear onto an axle or insert and turn a key in a lock. These goal-oriented tasks present a considerable challenge for reinforcement learning, since their natural reward function is sparse and prohibitive amounts of exploration are required to reach the goal and receive some learning signal. Past approaches tackle these problems by exploiting expert demonstrations or by manually designing a task-specific reward shaping function to guide the learning agent. Instead, we propose a method to learn these tasks without requiring any prior knowledge other than obtaining a single state in which the task is achieved. The robot is trained in reverse, gradually learning to reach the goal from a set of start states increasingly far from the goal. Our method automatically generates a curriculum of start states that adapts to the agent's performance, leading to efficient training on goal-oriented tasks. We demonstrate our approach on difficult simulated navigation and fine-grained manipulation problems, not solvable by state-of-the-art reinforcement learning methods.

Overview of "Reverse Curriculum Generation for Reinforcement Learning"

The paper "Reverse Curriculum Generation for Reinforcement Learning" by Florensa et al. presents a novel methodological approach to tackle goal-oriented tasks within the reinforcement learning (RL) domain. The authors address the challenge posed by sparse reward functions in complex tasks where traditional RL methods face limitations due to the sparse nature of the learning signal.

Key Contributions

The proposed method is grounded on three core assumptions: the ability to reset the environment to arbitrary states, access to at least one state where the goal is achieved, and the presence of a communicating Markov Chain in the task. With these assumptions, the authors introduce a process for adjusting the distribution of start states to enhance learning effectiveness. The primary innovation lies in the reverse training approach, where the agent begins learning from states near a successful goal state and progressively extends its understanding to states further away.

Key elements of this novel procedure include:

• Adaptive Start State Distribution: The method involves dynamically adapting the start state distribution at each iteration by sampling states from which the agent achieves partial success, termed as "good starts." This distribution is shaped to optimize the learning trajectory by focusing training where the policy is close to succeeding.
• Brownian Motion for State Exploration: To generate new start states, a localized exploration process through Brownian motion is employed. This enables the agent to consider "nearby” states without diverging into infeasible areas of the state space.
• Formal Framework and Practicality: The authors formalize a framework for automatic curriculum generation, automating the creation of progressively challenging learning environments. This is achieved by adjusting the difficulty level based on the agent's current capabilities, observed empirically through training iterations.

Experimental Validation

The authors validate their methodology on synthetic and articulated robotic manipulation tasks simulated in MuJoCo. The key tasks include navigational challenges such as a point-mass navigating a maze and more intricate manipulation tasks like inserting a key into a lock using a robotic arm.

The proposed method is compared against baseline RL techniques using uniform sampling from the environment's space. The results substantiate the efficacy of the reverse curriculum approach, showing significant improvement in learning efficiency and success rates, especially in scenarios where baseline methods fail due to sparse reward issues.

Implications

The implications of this research extend both theoretically and practically:

• Theoretical Perspective: The reverse curriculum strategy enhances our understanding of leveraging start state distributions to overcome sparse rewards, demonstrating a practical pathway to improve RL algorithms. The insights can potentially influence future research focused on complex task environments where task-specific reward engineering is impractical.
• Practical Deployment: In practical applications, particularly in robotics and autonomous systems, this method offers a scalable solution for training models to perform intricate sequences of operations without extensive prior shape engineering. This is particularly relevant in situations with limited human intervention for reward design or where dynamic task environments preclude extensive manual calibration.
• Future Directions: The work opens up avenues for integrating this approach with model-based methods, domain adaptation, and hierarchical reinforcement learning frameworks to further exploit curriculum learning paradigms. A potential area for exploration involves combining reverse curriculum learning with real-world deployment strategies, like sim-to-real transfer, by incorporating elements of domain randomization.

In summary, the reverse curriculum generation represents a substantial step forward in RL, particularly for tasks hindered by sparse rewards and complex dynamics. The direct focus on adapting the learning starting point embraces a pragmatic yet theoretically sound approach, enhancing learning rates and solution generalizability across broader state spaces.