Optimizing Sparse-Reward Environments with OPRL

Overview

In this tutorial, we explore Online Process Reward Learning (OPRL) and demonstrate how step-level reward signals can be learned from trajectory preferences to tackle sparse-reward reinforcement learning tasks.

Understanding OPRL

OPRL enables agents to learn dense rewards through preference-driven shaping. The process improves credit assignment and accelerates learning in challenging environments. By implementing OPRL, we aim to enhance policy optimization and performance.

Setting Up the Maze Environment

class MazeEnv:
    def __init__(self, size=8):
        self.size = size
        self.start = (0, 0)
        self.goal = (size-1, size-1)
        self.obstacles = set([(i, size//2) for i in range(1, size-2)])
        self.reset()
 
    def reset(self):
        self.pos = self.start
        self.steps = 0
        return self._get_state()
 
    # ... Additional methods for state handling and movement

The grid represents agent movements, obstacles, and goal states.

Building Reward and Policy Networks

We construct neural networks for process rewards and policy decisions using frameworks such as PyTorch. The subsequent designs optimize how states are represented and how decisions are influenced by learned process rewards.

Trajectory Collection and Action Selection

Implementing an ε-greedy strategy ensures exploration during action selection. As the agent navigates the maze, we store trajectories:

class OPRLAgent:
    def __init__(self, state_dim, action_dim, lr=3e-4):
        # Initialization of policy and reward models
 
    def select_action(self, state, epsilon=0.1):
        # Selection logic...

Learning Preferences

We generate preference pairs from trajectories to improve the reward model based on the experiences collected:

def generate_preference(self):
    # Preference generation logic

Training the Reward Model

The training of the reward model employs a standard loss function based on the preferences accumulated:

def train_reward_model(self, n_updates=5):
    # Training procedure...

Policy Training

We finally combine shaped rewards with a standard reinforcement learning framework to execute policy training:

def train_policy(self, n_updates=3, gamma=0.98):
    # Policy training logic...

Training Loop

The main training routine integrates exploration strategies while continuously updating preferences and policies.

Visualizing Results

It is key to plot the learning dynamics such as returns, success rates, and losses:

# Visualization code... 

Conclusion

OPRL provides effective online feedback, enhancing agent ability in sparse-reward environments. As demonstrated, it is adaptable for various RL settings. Through better understanding, larger mazes, and human feedback integration, OPRL reveals promising enhancements in reinforcement learning.