Initial commit: add project materials and code

code/configs/__init__.py

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+# configs/__init__.py

code/configs/default.yaml

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+# =============================================================================
+# Co-MADDPG Wireless Resource Allocation — Default Configuration
+# =============================================================================
+# All hyperparameters follow the paper's specifications for semantic-aware
+# cooperative multi-agent resource allocation in OFDMA systems.
+# =============================================================================
+
+env:
+  # OFDMA system parameters
+  num_subcarriers: 64              # N: total number of OFDM subcarriers
+  bandwidth: 10.0e+6                # B: total system bandwidth (Hz)
+  subcarrier_spacing: 156250.0     # Δf: subcarrier spacing (Hz), B/N
+  max_power: 1.0                   # P_max: maximum transmit power per user (W)
+  noise_psd: -174                  # N0: noise power spectral density (dBm/Hz)
+  carrier_freq: 3.5                # f_c: carrier frequency (GHz)
+
+  # Cell geometry
+  min_distance: 50                 # d_min: minimum BS-user distance (m)
+  max_distance: 500                # d_max: maximum BS-user distance (m)
+
+  # User configuration
+  num_semantic_users: 3            # K_s: number of semantic communication users
+  num_traditional_users: 3         # K_b: number of traditional bit-rate users
+
+  # QoS constraints
+  min_rate_req: 5.0e+5            # R_min: minimum rate requirement for traditional users (bps)
+
+  # Semantic compression ratio bounds
+  rho_max: 1.0                     # ρ_max: maximum compression ratio (no compression)
+  rho_min: 0.05                    # ρ_min: minimum compression ratio
+
+  # QoE weighting factors
+  w1: 0.7                          # w1: weight for semantic similarity (SSIM)
+  w2: 0.3                          # w2: weight for compression efficiency
+
+training:
+  # Episode configuration
+  max_episodes: 5000               # total training episodes
+  max_steps: 200                   # maximum steps per episode
+
+  # Replay buffer and sampling
+  batch_size: 256                  # mini-batch size for gradient updates
+  buffer_capacity: 100000          # replay buffer capacity
+
+  # Learning rates
+  actor_lr: 1.0e-4                 # actor network learning rate
+  critic_lr: 3.0e-4                # critic network learning rate
+
+  # Discount and soft-update
+  gamma: 0.95                      # discount factor γ
+  tau: 0.01                        # soft target update rate τ
+
+  # Ornstein-Uhlenbeck exploration noise
+  ou_sigma_init: 0.2               # initial noise standard deviation
+  ou_sigma_min: 0.01               # minimum noise standard deviation
+  ou_theta: 0.15                   # OU mean-reversion rate θ
+
+  # Cooperative mechanism parameters
+  beta: 5.0                        # β: cooperation benefit scaling factor
+  q_threshold: 0.6                 # Q-value threshold for cooperation mode switch
+  update_interval: 5               # target network update interval (episodes)
+
+  # Reproducibility
+  seed: 42
+
+network:
+  # Actor network hidden layer dimensions
+  actor_hidden: [256, 256, 128]
+
+  # Critic network hidden layer dimensions
+  critic_hidden: [512, 512, 256]
+
+reward:
+  # Cooperative mode reward weights
+  coop_self: 0.5                   # α_self: weight on own reward (cooperative)
+  coop_other: 0.3                  # α_other: weight on other agents' reward
+  coop_sys: 0.2                    # α_sys: weight on system-level reward
+
+  # Competitive mode reward weights
+  comp_self: 0.8                   # α_self: weight on own reward (competitive)
+  comp_sys: 0.2                    # α_sys: weight on system-level reward