102 lines
3.7 KiB
Python
102 lines
3.7 KiB
Python
import numpy as np
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"""
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Baseline: EqualAllocation (等额分配基线)
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=====================================
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Purpose (lower bound):
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- This baseline represents a simple heuristic approach with no learning involved.
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- It serves as a lower bound for performance comparison, showing the system behavior under a naive, fixed resource allocation strategy.
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- 目的（性能下限）：该基线代表了一种不涉及学习的简单启发式方法。它作为性能对比的下限，展示了在朴素的固定资源分配策略下系统的表现。
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Difference from Co-MADDPG:
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1. Learning: No learning vs Deep Reinforcement Learning.
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2. Action Selection: Always fixed at [0.5, 0.5, 0.5] for all resource parameters (subcarrier fraction, power, m_param).
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3. 与 Co-MADDPG 的区别：
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- 学习机制：无学习 vs 深度强化学习。
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- 动作选择：所有资源参数（子载波比例、功率、m 参数）始终固定为 [0.5, 0.5, 0.5]。
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Contribution:
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- Contributes to performance baseline tables as the "Random/Fixed" comparison point.
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- 贡献：作为“随机/固定”对比点，用于性能基准表。
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"""
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class DummyBuffer:
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"""
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Dummy replay buffer that satisfies train.py's push/len interface.
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满足 train.py 中 push/len 接口要求的虚拟重放池。
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"""
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def push(self, *args):
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# Do nothing as no learning is performed
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# 不执行任何操作，因为没有学习过程
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pass
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def __len__(self):
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# Always return 0 to indicate no samples available
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# 始终返回 0，表示没有可用样本
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return 0
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class EqualAllocation:
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"""
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EqualAllocation algorithm implementation.
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等额分配算法实现。
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"""
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def __init__(self, config):
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# Initialize with configuration and a dummy buffer
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# 使用配置和虚拟重放池进行初始化
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self.config = config
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self.replay_buffer = DummyBuffer()
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def select_action(self, obs_s, obs_b, explore=True):
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"""
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Always return a fixed action [0.5, 0.5, 0.5].
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始终返回固定动作 [0.5, 0.5, 0.5]。
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"""
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return np.array([0.5, 0.5, 0.5], dtype=np.float32), \
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np.array([0.5, 0.5, 0.5], dtype=np.float32)
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def compute_rewards(self, qoe_s, qoe_b, qoe_sys):
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"""
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Compute rewards using a fixed λ=0.5 for consistency in monitoring.
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使用固定 λ=0.5 计算奖励，以保持监测的一致性。
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Formula: Balanced combination of coop and comp components.
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公式说明：协作项与竞争项的平衡组合。
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"""
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lam = 0.5
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rew_cfg = self.config.get('reward', {})
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coop_self = rew_cfg.get('coop_self', 0.5)
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coop_other = rew_cfg.get('coop_other', 0.3)
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coop_sys = rew_cfg.get('coop_sys', 0.2)
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comp_self = rew_cfg.get('comp_self', 0.8)
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comp_sys = rew_cfg.get('comp_sys', 0.2)
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# Compute reward components for S
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# 计算 S 的奖励组成部分
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r_coop_s = coop_self * qoe_s + coop_other * qoe_b + coop_sys * qoe_sys
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r_comp_s = comp_self * qoe_s + comp_sys * qoe_sys
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r_s = lam * r_coop_s + (1 - lam) * r_comp_s
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# Compute reward components for B
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# 计算 B 的奖励组成部分
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r_coop_b = coop_self * qoe_b + coop_other * qoe_s + coop_sys * qoe_sys
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r_comp_b = comp_self * qoe_b + comp_sys * qoe_sys
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r_b = lam * r_coop_b + (1 - lam) * r_comp_b
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return r_s, r_b, lam
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def update(self):
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"""
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No update performed in heuristic baseline.
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启发式基线中不执行更新。
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"""
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return None
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def save(self, path):
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"""No state to save."""
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pass
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def load(self, path):
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"""No state to load."""
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pass
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