""" This file to plot the partial and full curves for all algorithms in the same plot for all environments. """ import jax.numpy as jnp import matplotlib.pyplot as plt import numpy as np import pandas as pd import seaborn as sns from jax import lax from scipy.interpolate import interp1d import wandb def f(name): WINDOW_SIZE = 100 SIGMA = 100 INTERP_POINTS = 1000 NORMALIZING_FACTOR = 200 ENV_MAX_STEPS = { "CountRecallEasy": 1e8, "CountRecallMedium": 1e8, "CountRecallHard": 1e8, "BattleShipEasy": 1e8, "BattleShipMedium": 1e8, "BattleShipHard": 1e8, # other environments with default max steps 1e7 } AXIS_FONT = {"fontsize": 9, "labelpad": 8} TICK_FONT = {"labelsize": 8} api = wandb.Api() runs = api.runs("bolt-um/Arcade-RLC-Grad") filtered_runs = [run for run in runs if run.state == "finished"] print(f"Total runs: {len(runs)}, Completed runs: {len(filtered_runs)}") METRIC_MAPPING = { "PQN": {"return_col": "returned_episode_returns", "time_col": "env_step"}, "PQN_RNN": {"return_col": "returned_episode_returns", "time_col": "env_step"}, "default": {"return_col": "episodic return", "time_col": "TOTAL_TIMESTEPS"}, } def process_run(run): """Process individual W&B run with dynamic max steps per environment""" try: config = {k: v for k, v in run.config.items() if not k.startswith("_")} env_name = config.get("ENV_NAME", "UnknownEnv") partial_status = str(config.get("PARTIAL", False)) if env_name in ENV_MAX_STEPS: env_max_step = ENV_MAX_STEPS[env_name] else: env_max_step = 1e8 alg_name = config.get("ALG_NAME", "").upper() memory_type = "MLP" if alg_name == "PQN_RNN": memory_type = config.get("MEMORY_TYPE", "Unknown").capitalize() metric_map = METRIC_MAPPING.get(alg_name, METRIC_MAPPING["default"]) # history = run.history(keys=[metric_map["return_col"], metric_map["time_col"]]) history = list( run.scan_history( keys=[metric_map["return_col"], metric_map["time_col"]] ) ) history = pd.DataFrame( history, columns=[metric_map["return_col"], metric_map["time_col"]] ) history["true_steps"] = history[metric_map["time_col"]].clip( upper=env_max_step ) history = history.sort_values(metric_map["time_col"]).drop_duplicates( subset=["true_steps"] ) if len(history) < 2: print(f"Skipping {run.name} due to insufficient data points") return None # Get first and last values for extrapolation first_return = history[metric_map["return_col"]].iloc[0] last_return = history[metric_map["return_col"]].iloc[-1] # Create unified interpolation grid for this environment unified_steps = np.linspace(0, env_max_step, INTERP_POINTS) unified_steps = np.round(unified_steps, decimals=5) scale_factor = NORMALIZING_FACTOR / env_max_step # Interpolate returns to uniform grid interp_func = interp1d( history["true_steps"], history[metric_map["return_col"]], kind="linear", bounds_error=False, fill_value=(first_return, last_return), ) interpolated_returns = interp_func(unified_steps) smoothed_returns = ( pd.Series(interpolated_returns) .ewm(span=100, adjust=False, min_periods=1) .mean() .values ) # Compute cumulative maximum using JAX cummax_returns = lax.cummax(jnp.array(smoothed_returns)) return pd.DataFrame( { "Algorithm": f"{alg_name} ({memory_type})", "Return": interpolated_returns, "Smoothed Return": smoothed_returns, "Cummax Return": np.array(cummax_returns), # Convert back to NumPy "True Steps": unified_steps, "EnvName": env_name, "Partial": partial_status, "Seed": str(config.get("SEED", 0)), "run_id": run.id, "StepsNormalized": unified_steps / env_max_step, "EnvMaxStep": env_max_step, "ScaleFactor": scale_factor, } ) except Exception as e: print(f"Error processing {run.name}: {str(e)}") return None # Process all runs and combine data # all_data = [df for run in filtered_runs if (df := process_run(run)) is not None] # if not all_data: # print("No valid data to process") # exit() # runs_df = pd.concat(all_data, ignore_index=True) # runs_df.to_pickle("rlcgrad.pkl") runs_df = pd.read_pickle("rlcgrad.pkl") def plot_comparative_curves(runs_df, name): """Plot comparative curves for all environments in a single plot""" runs_df["EnvBaseName"] = runs_df["EnvName"].apply( lambda x: x.replace("Easy", "").replace("Medium", "").replace("Hard", "") ) envs = runs_df["EnvBaseName"].unique() palette = sns.color_palette("husl", len(envs)) env_color_map = dict(zip(envs, palette)) partial_map = {"True": "POMDP", "False": "MDP"} max_step = 1e8 plt.figure(figsize=(12, 7)) sns.set() plt.text( 1, -0.15, f"{max_step:.0e}".replace("+", "").replace("0", ""), transform=plt.gca().transAxes, ha="right", va="top", fontsize=35, color="#666666", bbox=dict(facecolor="white", alpha=0.8, edgecolor="none", pad=2), ) pomdp_handles = [] pomdp_labels = [] mdp_handles = [] mdp_labels = [] for env_base in envs: for partial_status in ["False", "True"]: data = runs_df[ (runs_df["EnvBaseName"] == env_base) & (runs_df["Partial"] == partial_status) ] if not data.empty: color = env_color_map[env_base] line_style = "--" if partial_status == "True" else "-" label = f"{env_base} - {partial_map[partial_status]}" line = plt.plot( data["StepsNormalized"], data["Cummax Return"], color=color, linewidth=2.5, linestyle=line_style, label=label, )[0] if partial_status == "True": pomdp_handles.append(line) pomdp_labels.append(label) else: mdp_handles.append(line) mdp_labels.append(label) plt.xlabel("Env Steps", fontsize=35) plt.ylabel("Episodic Return", fontsize=35) plt.tick_params(axis="both", which="major", labelsize=35) plt.grid(True, alpha=0.5) handles = mdp_handles + pomdp_handles labels = mdp_labels + pomdp_labels plt.legend(handles, labels, loc="best", fontsize=22, ncol=2) plt.title("LRU", fontsize=35, pad=12, fontweight="semibold") plt.tight_layout() plt.savefig( "{}.pdf".format(name), dpi=300, bbox_inches="tight", facecolor="white" ) plt.close() plot_comparative_curves(runs_df, name) for i in range(1): f(f"rlcgrad{i}")