Policy Gradient Methods in Partially Observable Markov Decision Processes (POMDPs)

Applying policy gradient methods in Partially Observable Markov Decision Processes (POMDPs) represents a fundamental reinforcement learning technique. In the POMDP framework, agents cannot directly observe environmental states and must infer current states through observation signals, which significantly increases the complexity of policy learning. Policy gradient methods optimize directly in the policy space by computing gradients of policy performance to update policy parameters. In POMDP settings, policies typically depend on the entire observation history or utilize structures like recurrent neural networks to maintain internal state representations. When implementing in MATLAB, several key considerations emerge: First, establishing the POMDP model requires defining state transition probabilities, observation probabilities, and reward functions. Then designing the policy network architecture, which could range from simple linear functions to deep neural networks. Implementation must pay special attention to gradient computation, typically using likelihood ratio methods or REINFORCE algorithms to calculate policy gradients. Practical code implementation should include several core modules: environment simulator, policy network, gradient calculator, and parameter updater. The environment simulator generates states, observations, and rewards; the policy network makes decisions based on current observations and historical information; the gradient calculator estimates gradients through sampled trajectories; and the parameter updater adjusts policy parameters based on gradient information. The training process typically follows an episodic approach, where each round collects multiple trajectory samples before computing average gradients for policy updates. To reduce variance, techniques like baseline functions or advantage functions can be incorporated. Upon convergence, the resulting policy can make reasonable decisions in partially observable environments. Key implementation details include using MATLAB's neural network toolbox for policy network design, implementing trajectory sampling with proper episode termination conditions, and applying gradient clipping or normalization for stable training. The REINFORCE algorithm implementation requires careful handling of log probability calculations and advantage estimation for effective policy updates.