This document discusses strategies for building a multi-threaded game server architecture. It shows how using multiple threads to handle different game logic tasks in parallel can improve performance over a single-threaded approach. Some key challenges discussed include avoiding race conditions through synchronization, preventing deadlocks, and maintaining thread-safety when accessing shared resources. The document provides examples of common threading patterns that can be applied like worker threads, producer-consumer queues, and separating game logic by zone, room, and player entities.
This document discusses strategies for building a multi-threaded game server architecture. It shows how using multiple threads to handle different game logic tasks in parallel can improve performance over a single-threaded approach. Some key challenges discussed include avoiding race conditions through synchronization, preventing deadlocks, and maintaining thread-safety when accessing shared resources. The document provides examples of common threading patterns that can be applied like worker threads, producer-consumer queues, and separating game logic by zone, room, and player entities.
Continuous Control with Deep Reinforcement Learning, lillicrap et al, 2015Chris Ohk
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The paper introduces Deep Deterministic Policy Gradient (DDPG), a model-free reinforcement learning algorithm for problems with continuous action spaces. DDPG combines actor-critic methods with experience replay and target networks similar to DQN. It uses a replay buffer to minimize correlations between samples and target networks to provide stable learning targets. The algorithm was able to solve challenging control problems with high-dimensional observation and action spaces, demonstrating the ability of deep reinforcement learning to handle complex, continuous control tasks.