""" UGTC: Uncertainty-Gated Temporal Credit — Interactive Demo =========================================================== This Gradio app provides an educational interface for understanding the UGTC algorithm, its mathematics, and its RL integrations. Paper: https://doi.org/10.5281/zenodo.19715116 GitHub: https://github.com/ethosoftai/ugtc """ import numpy as np import torch import torch.nn as nn import gradio as gr import plotly.graph_objects as go import plotly.express as px from plotly.subplots import make_subplots # ── UGTC Core (self-contained, no external dep) ─────────────────────────────── def sigmoid(x): return 1.0 / (1.0 + np.exp(-x)) def compute_gate(sigma, sigma_ema, beta=5.0, eps=1e-8): """Compute UGTC uncertainty gate u(s) = sigmoid(-β·(σ̂ - 1)).""" sigma_hat = sigma / (sigma_ema + eps) return sigmoid(-beta * (sigma_hat - 1.0)) def compute_gae(rewards, values, next_values, dones, gamma=0.99, lam=0.9): """Standard GAE computation.""" T = len(rewards) advantages = np.zeros(T) gae = 0.0 for t in reversed(range(T)): delta = rewards[t] + gamma * next_values[t] * (1 - dones[t]) - values[t] gae = delta + gamma * lam * (1 - dones[t]) * gae advantages[t] = gae return advantages def blend_advantages(adv_fast, adv_slow, gates): """A^UGTC = u(s)·A^slow + (1-u(s))·A^fast""" return gates * adv_slow + (1 - gates) * adv_fast # ── Tab 1: Gate Visualizer ──────────────────────────────────────────────────── def plot_gate_curve(beta, sigma_ema): """Plot gate u(s) as a function of ensemble disagreement σ(s).""" sigma_vals = np.linspace(0, 3 * sigma_ema, 500) gate_vals = compute_gate(sigma_vals, sigma_ema, beta=beta) fig = go.Figure() fig.add_trace(go.Scatter( x=sigma_vals, y=gate_vals, mode='lines', name='u(σ)', line=dict(color='#6366f1', width=3), )) fig.add_hline(y=0.5, line_dash='dot', line_color='#94a3b8', annotation_text='u = 0.5 (equal blend)') fig.add_vline(x=float(sigma_ema), line_dash='dash', line_color='#10b981', annotation_text=f'σ_EMA = {sigma_ema:.2f}') # Shade regions fig.add_vrect(x0=0, x1=float(sigma_ema), fillcolor='rgba(16,185,129,0.08)', line_width=0, annotation_text='Use A^slow', annotation_position='top left') fig.add_vrect(x0=float(sigma_ema), x1=float(3 * sigma_ema), fillcolor='rgba(239,68,68,0.06)', line_width=0, annotation_text='Use A^fast', annotation_position='top right') fig.update_layout( title=f'UGTC Gate u(σ) — β={beta:.1f}, σ_EMA={sigma_ema:.2f}', xaxis_title='Ensemble Disagreement σ(s)', yaxis_title='Gate Value u(s)', yaxis=dict(range=[-0.05, 1.05]), template='plotly_dark', height=420, plot_bgcolor='rgba(17,24,39,1)', paper_bgcolor='rgba(17,24,39,1)', font=dict(color='#f1f5f9'), ) return fig def gate_demo(beta, sigma_ema, current_sigma): """Show current gate value for a specific σ(s) point.""" gate = float(compute_gate(current_sigma, sigma_ema, beta=beta)) fig = plot_gate_curve(beta, sigma_ema) # Mark the current point fig.add_trace(go.Scatter( x=[current_sigma], y=[gate], mode='markers', name='Current state', marker=dict(color='#f59e0b', size=14, symbol='star'), )) blend_desc = "slow (accurate)" if gate > 0.6 else ("fast (stable)" if gate < 0.4 else "equal blend") action = f"→ {'more' if gate > 0.6 else 'less'} weight on slow estimate" summary = f""" **Current state:** - σ(s) = {current_sigma:.3f} | σ_EMA = {sigma_ema:.3f} | σ̂ = {current_sigma/sigma_ema:.3f} - Gate u(s) = **{gate:.4f}** - Blending: {gate:.1%} slow + {1-gate:.1%} fast - Decision: use **{blend_desc}** estimate """ return fig, summary # ── Tab 2: Advantage Blending ───────────────────────────────────────────────── def plot_advantage_blend(lambda_fast, lambda_slow, beta, seed): """Simulate UGTC advantage blending over a trajectory.""" rng = np.random.default_rng(int(seed)) T = 64 gamma = 0.99 rewards = rng.normal(0, 1, T) v_fast = rng.normal(0, 0.5, T) v_slow1 = v_fast + rng.normal(0, 0.3, T) v_slow2 = v_fast + rng.normal(0, 0.3, T) v_slow3 = v_fast + rng.normal(0, 0.3, T) v_slow_mean = (v_slow1 + v_slow2 + v_slow3) / 3 sigma = np.std([v_slow1, v_slow2, v_slow3], axis=0) nv_fast = np.roll(v_fast, -1); nv_fast[-1] = 0 nv_slow = np.roll(v_slow_mean, -1); nv_slow[-1] = 0 dones = np.zeros(T); dones[-1] = 1.0 adv_fast = compute_gae(rewards, v_fast, nv_fast, dones, gamma, lambda_fast) adv_slow = compute_gae(rewards, v_slow_mean, nv_slow, dones, gamma, lambda_slow) sigma_ema = float(sigma.mean()) gates = compute_gate(sigma, sigma_ema, beta=beta) adv_ugtc = blend_advantages(adv_fast, adv_slow, gates) t = np.arange(T) fig = make_subplots( rows=3, cols=1, subplot_titles=[ 'Advantages (Fast vs Slow vs UGTC Blended)', 'Uncertainty Gate u(s) over Trajectory', 'Ensemble Disagreement σ(s)', ], vertical_spacing=0.1, ) fig.add_trace(go.Scatter(x=t, y=adv_fast, name='A^fast (λ={:.2f})'.format(lambda_fast), line=dict(color='#f59e0b', width=1.5, dash='dot')), row=1, col=1) fig.add_trace(go.Scatter(x=t, y=adv_slow, name='A^slow (λ={:.2f})'.format(lambda_slow), line=dict(color='#06b6d4', width=1.5, dash='dash')), row=1, col=1) fig.add_trace(go.Scatter(x=t, y=adv_ugtc, name='A^UGTC (blended)', line=dict(color='#6366f1', width=2.5)), row=1, col=1) fig.add_trace(go.Scatter(x=t, y=gates, name='Gate u(s)', fill='tozeroy', line=dict(color='#10b981'), fillcolor='rgba(16,185,129,0.15)'), row=2, col=1) fig.add_trace(go.Bar(x=t, y=sigma, name='σ(s)', marker_color='#8b5cf6', opacity=0.7), row=3, col=1) fig.update_layout( template='plotly_dark', height=700, plot_bgcolor='rgba(17,24,39,1)', paper_bgcolor='rgba(17,24,39,1)', font=dict(color='#f1f5f9'), showlegend=True, ) fig.update_xaxes(title_text='Timestep') stats = f""" **Trajectory Statistics (T={T}):** - A^fast: mean={adv_fast.mean():.3f}, std={adv_fast.std():.3f} - A^slow: mean={adv_slow.mean():.3f}, std={adv_slow.std():.3f} - A^UGTC: mean={adv_ugtc.mean():.3f}, std={adv_ugtc.std():.3f} - Mean gate u(s): {gates.mean():.3f} → {gates.mean()*100:.0f}% slow weight on average - σ_EMA: {sigma_ema:.4f} """ return fig, stats # ── Tab 3: Algorithm Comparison ─────────────────────────────────────────────── ALGO_CONTENT = { "UGTC-PPO": { "description": """ ## UGTC-PPO Integration **Key change from vanilla PPO:** Advantages are computed by `UGTCModule.compute_advantages()` instead of standard single-critic GAE. **Actor loss (unchanged PPO clipped surrogate):** ``` ratio = π_θ(a|s) / π_θ_old(a|s) L_policy = -mean(min(ratio · Â^UGTC, clip(ratio, 1±ε) · Â^UGTC)) ``` **Critic losses (train all UGTC critics):** ``` L_fast = MSE(V_fast(s), R̂) L_slow = mean over m of MSE(Vᵐ_slow(s), R̂) L_total = L_policy + 0.5·(L_fast + L_slow) ``` Where `R̂ = Â^UGTC + V^UGTC(s)` are the value targets. **What stays the same:** PPO clip coefficient ε, entropy bonus, gradient clipping, rollout collection — all unchanged. **Overhead:** 3 extra MLP value heads (fast + 3 slow). Negligible relative to actor network. """, "pseudocode": """ REPEAT until convergence: τ ← collect_rollout(π_θ) # same as vanilla PPO # === KEY CHANGE: UGTC advantage === A^UGTC ← ugtc.compute_advantages(τ, γ) Â ← normalize(A^UGTC) R̂ ← Â + V^UGTC(s) FOR k = 1 TO K: # standard PPO epochs ratio ← π_θ(a|s) / π_old(a|s) L_policy ← clipped_surrogate(ratio, Â) L_critics ← MSE(V_fast, R̂) + MSE(V_slow, R̂) step(L_policy + 0.5·L_critics) """ }, "UGTC-TD3": { "description": """ ## UGTC-TD3 Integration **Key change from vanilla TD3:** The actor receives a UGTC baseline correction. **Actor loss (modified):** ``` Q_min = min(Q¹(s, π(s)), Q²(s, π(s))) V^UGTC(s) = u(s)·V̄_slow(s) + (1-u(s))·V_fast(s) A^UGTC(s) = Q_min - V^UGTC(s) L_actor = -mean(Q_min + η·A^UGTC(s)) ``` **Critic loss (unchanged standard TD3 twin-Q):** ``` ỹ = r + γ·min(Q¹_target, Q²_target)(s', π_target(s') + ε) L_critic = MSE(Q¹(s,a), ỹ) + MSE(Q²(s,a), ỹ) ``` **What stays the same:** Twin-Q, target policy smoothing, delayed actor update, soft target updates — all unchanged. **Note:** η=0.5 (correction weight) is an implementation default, not a fixed UGTC hyperparameter. May benefit from tuning. """, "pseudocode": """ REPEAT: B = sample(replay_buffer) # Critic update (standard TD3 — unchanged) ỹ = r + γ·min(Q_target)(s', π_target(s') + ε) step(MSE(Q¹(s,a), ỹ) + MSE(Q²(s,a), ỹ)) IF step % policy_delay == 0: # === KEY CHANGE: UGTC baseline correction === Q_min = min(Q¹(s, π(s)), Q²(s, π(s))) V_ugtc = u(s)·V̄_slow + (1-u(s))·V_fast A_ugtc = Q_min - V_ugtc step(-mean(Q_min + η·A_ugtc)) # actor loss soft_update(targets) """ }, "UGTC-SAC": { "description": """ ## UGTC-SAC Integration **Key change from vanilla SAC:** V^UGTC replaces the implicit value baseline in the actor loss. **Standard SAC actor loss:** ``` L_π = mean(α·log π(a|s) - Q_min(s, a)) ``` **UGTC-SAC actor loss:** ``` V^UGTC(s) = u(s)·V̄_slow(s) + (1-u(s))·V_fast(s) L_π^UGTC = mean(α·log π(a|s) - Q_min(s, a) + V^UGTC(s)) ``` By adding V^UGTC(s), the actor's gradient is centered around a more accurate baseline, reducing variance. **What stays the same:** Entropy coefficient α, twin-Q critic training, automatic entropy tuning, target network updates — all unchanged. """, "pseudocode": """ REPEAT: B = sample(replay_buffer) # Critic update (standard SAC — unchanged) ã' = sample π(·|s') y = r + γ(Q_min_target(s',ã') - α·log π(ã'|s')) step(MSE(Q¹(s,a), y) + MSE(Q²(s,a), y)) # === KEY CHANGE: V^UGTC as actor baseline === ã, log_π = sample π(·|s) Q_min = min(Q¹(s,ã), Q²(s,ã)) V_ugtc = u(s)·V̄_slow + (1-u(s))·V_fast # Standard: mean(α·log π - Q_min) # UGTC-SAC: mean(α·log π - Q_min + V_ugtc) step(mean(α·log_π - Q_min + V_ugtc)) """ }, } def show_algorithm(algo_name): content = ALGO_CONTENT[algo_name] return content["description"], f"```\n{content['pseudocode']}\n```" # ── Tab 4: Hyperparameter Explorer ──────────────────────────────────────────── def plot_beta_effect(beta_values_str): """Show how different β values affect gate sharpness.""" sigma_range = np.linspace(0, 3, 400) sigma_ema = 1.0 beta_list = [float(b.strip()) for b in beta_values_str.split(',') if b.strip()] colors = ['#6366f1', '#10b981', '#f59e0b', '#ef4444', '#06b6d4'] fig = go.Figure() for i, beta in enumerate(beta_list[:5]): gates = compute_gate(sigma_range, sigma_ema, beta=beta) fig.add_trace(go.Scatter( x=sigma_range, y=gates, name=f'β = {beta}', line=dict(color=colors[i % len(colors)], width=2.5), )) fig.add_vline(x=1.0, line_dash='dash', line_color='#94a3b8', annotation_text='σ_EMA = 1.0') fig.add_hline(y=0.5, line_dash='dot', line_color='#94a3b8') fig.update_layout( title='Gate Sharpness: Effect of Temperature β', xaxis_title='Normalized Disagreement σ̂(s) = σ(s)/σ_EMA', yaxis_title='Gate Value u(s)', yaxis=dict(range=[-0.05, 1.05]), template='plotly_dark', height=420, plot_bgcolor='rgba(17,24,39,1)', paper_bgcolor='rgba(17,24,39,1)', font=dict(color='#f1f5f9'), ) desc = ( "**β interpretation:**\n" "- Low β (e.g. 0.5): Gradual, smooth blending\n" "- β = 5.0 (paper default): Moderate sharpness, responsive to uncertainty\n" "- High β (e.g. 20): Near-binary switching between fast and slow\n\n" "The paper uses β = 5.0 across all benchmarks without tuning." ) return fig, desc def plot_lambda_effect(): """Show how λ_fast vs λ_slow affects GAE horizon.""" T = 50 gamma = 0.99 t = np.arange(T) fig = go.Figure() for lam, name, color in [ (0.80, 'λ_fast=0.80 (low bias-var tradeoff)', '#f59e0b'), (0.95, 'λ=0.95 (standard GAE)', '#94a3b8'), (0.99, 'λ_slow=0.99 (long horizon)', '#6366f1'), ]: # Effective weight of step k in GAE: (γλ)^k weights = np.array([(gamma * lam) ** k for k in range(T)]) weights /= weights.sum() fig.add_trace(go.Scatter(x=t, y=weights, name=name, line=dict(color=color, width=2.5))) fig.update_layout( title='GAE Credit Attribution Weights by λ Value', xaxis_title='Steps into future (k)', yaxis_title='Relative weight of δₜ₊ₖ in Aₜ', template='plotly_dark', height=400, plot_bgcolor='rgba(17,24,39,1)', paper_bgcolor='rgba(17,24,39,1)', font=dict(color='#f1f5f9'), ) return fig # ── Tab 5: Math ─────────────────────────────────────────────────────────────── MATH_SECTIONS = { "GAE Foundation": """ ## Generalized Advantage Estimation The TD residual at step t: **δₜ = rₜ + γ·V(sₜ₊₁)·(1 - dₜ) - V(sₜ)** The GAE advantage (reverse accumulation): **Aₜ^GAE(λ) = Σₖ₌₀^∞ (γλ)ᵏ · δₜ₊ₖ** This is a weighted sum of k-step TD residuals. The λ parameter controls the effective horizon: - **λ → 0**: Pure TD(0) — low variance, high bias (myopic) - **λ → 1**: Monte Carlo — low bias, high variance (long-sighted) UGTC exploits this by maintaining two separate GAE streams with different λ values. """, "Dual Critics": """ ## UGTC Dual-Critic Architecture **Fast critic** (single MLP): ``` V_fast: obs_dim → 64 → 64 → 1 A^fast_t = GAE(τ, V=V_fast, λ=λ_fast=0.80) ``` Characteristic: Low variance (single point estimate), higher bias **Slow ensemble** (M=3 independent MLPs): ``` V¹_slow, V²_slow, V³_slow (same arch, different random init) V̄_slow = mean(V¹, V², V³) A^slow_t = GAE(τ, V=V̄_slow, λ=λ_slow=0.99) ``` Characteristic: Lower bias (long horizon + ensemble mean), higher variance **Key insight:** The two streams provide complementary error profiles. The gate selects between them based on per-state confidence. """, "Uncertainty Gate": """ ## The Uncertainty Gate **Step 1** — Measure ensemble disagreement: ``` σ(s) = std(V¹_slow(s), V²_slow(s), V³_slow(s)) ``` High σ means the ensemble members disagree → the slow estimate is unreliable. **Step 2** — EMA normalization (momentum α = 0.99): ``` σ_EMA ← α · σ_EMA + (1-α) · E[σ(s)] σ̂(s) = σ(s) / (σ_EMA + ε) ``` This converts absolute disagreement to a relative measure. σ̂ > 1 means "above-average uncertainty." **Step 3** — Sigmoid gate (temperature β = 5.0): ``` u(s) = sigmoid(-β · (σ̂(s) - 1.0)) ``` When σ̂ = 1 (average uncertainty): u = 0.5 (equal blend) When σ̂ < 1 (below average): u → 1 (use slow) When σ̂ > 1 (above average): u → 0 (use fast) """, "Blended Advantage": """ ## UGTC Blended Advantage The final blended advantage: **A^UGTC_t = u(sₜ) · A^slow_t + (1 - u(sₜ)) · A^fast_t** And the blended value: **V^UGTC(s) = u(s) · V̄_slow(s) + (1-u(s)) · V_fast(s)** **Intuition:** - State well-understood (ensemble agrees, σ̂ < 1) → u(s) → 1 → use long-horizon A^slow - State uncertain (ensemble disagrees, σ̂ > 1) → u(s) → 0 → use stable A^fast This is analogous to Bayesian model averaging, but with a deterministic gate based on functional disagreement rather than posterior uncertainty. **In PPO:** `A^UGTC` directly replaces the GAE advantage in the clipped surrogate. **In TD3/SAC:** `V^UGTC` replaces the value baseline in the actor gradient. """, } def show_math(section): return MATH_SECTIONS[section] # ── Layout ──────────────────────────────────────────────────────────────────── HEADER = """

🎯 UGTC: Uncertainty-Gated Temporal Credit

A plug-in advantage estimator for actor-critic reinforcement learning

📄 Paper (Zenodo) ⭐ GitHub 📚 Full Docs

Accepted · Ulysseus Young Explorers in Science (UYES) Journal · Journal DOI forthcoming

""" OVERVIEW = """ ### What is UGTC? **UGTC** resolves a fundamental tension in reinforcement learning: standard GAE requires committing to a fixed λ, trading off bias vs. variance globally. UGTC instead maintains **two critics** with different λ values and dynamically blends them using an **uncertainty gate** driven by ensemble disagreement: | Stream | Critic | λ | Characteristic | |--------|--------|---|----------------| | Fast | Single MLP | **0.80** | Low variance, higher bias | | Slow | M=3 ensemble | **0.99** | Lower bias, higher variance | The gate `u(s) = σ(-β·(σ̂(s) - 1))` selects between them per-state: - **Low uncertainty** → `u → 1` → use slow (accurate) - **High uncertainty** → `u → 0` → use fast (stable) **Hyperparameters are fixed across all benchmarks** — no per-task tuning. """ CSS = """ body { font-family: 'Inter', sans-serif; } .gradio-container { max-width: 1200px; } .tab-nav button { font-weight: 600; } .output-markdown { font-size: 0.95rem; line-height: 1.7; } """ with gr.Blocks(css=CSS, title="UGTC — Uncertainty-Gated Temporal Credit") as demo: gr.HTML(HEADER) gr.Markdown(OVERVIEW) with gr.Tabs(): # ── Tab 1: Gate Visualizer ────────────────────────────────────────── with gr.TabItem("🎛️ Gate Visualizer"): gr.Markdown("### Explore how the UGTC uncertainty gate responds to ensemble disagreement") with gr.Row(): with gr.Column(scale=1): beta_slider = gr.Slider(0.5, 20.0, value=5.0, step=0.5, label="β — Gate Temperature (paper default: 5.0)") ema_slider = gr.Slider(0.1, 3.0, value=1.0, step=0.05, label="σ_EMA — Running Mean Uncertainty") sigma_slider = gr.Slider(0.0, 3.0, value=0.8, step=0.05, label="σ(s) — Current State's Ensemble Disagreement") run_btn = gr.Button("Update Gate", variant="primary") with gr.Column(scale=2): gate_plot = gr.Plot(label="Gate Curve") gate_info = gr.Markdown() run_btn.click(gate_demo, [beta_slider, ema_slider, sigma_slider], [gate_plot, gate_info]) demo.load(gate_demo, [beta_slider, ema_slider, sigma_slider], [gate_plot, gate_info]) # ── Tab 2: Advantage Blending ──────────────────────────────────────── with gr.TabItem("📊 Advantage Blending"): gr.Markdown("### See UGTC blend fast/slow advantages across a simulated trajectory") with gr.Row(): lf = gr.Slider(0.5, 1.0, value=0.80, step=0.01, label="λ_fast (paper default: 0.80)") ls = gr.Slider(0.5, 1.0, value=0.99, step=0.01, label="λ_slow (paper default: 0.99)") beta_b = gr.Slider(0.5, 20.0, value=5.0, step=0.5, label="β — Gate Temperature") seed_b = gr.Number(value=42, label="Random Seed", precision=0) blend_btn = gr.Button("Generate Trajectory", variant="primary") blend_plot = gr.Plot() blend_stats = gr.Markdown() blend_btn.click(plot_advantage_blend, [lf, ls, beta_b, seed_b], [blend_plot, blend_stats]) demo.load(plot_advantage_blend, [lf, ls, beta_b, seed_b], [blend_plot, blend_stats]) # ── Tab 3: Algorithm Comparison ────────────────────────────────────── with gr.TabItem("🔗 RL Integrations"): gr.Markdown("### How UGTC integrates with different RL algorithms") algo_select = gr.Radio( ["UGTC-PPO", "UGTC-TD3", "UGTC-SAC"], value="UGTC-PPO", label="Algorithm", ) algo_desc = gr.Markdown() algo_code = gr.Markdown() algo_select.change(show_algorithm, algo_select, [algo_desc, algo_code]) demo.load(show_algorithm, algo_select, [algo_desc, algo_code]) # ── Tab 4: Hyperparameter Explorer ────────────────────────────────── with gr.TabItem("⚙️ Hyperparameters"): gr.Markdown("### Visualize how UGTC hyperparameters affect behavior") with gr.Row(): with gr.Column(): gr.Markdown("#### Gate Temperature β") beta_input = gr.Textbox( value="0.5, 1.0, 5.0, 10.0, 20.0", label="β values (comma-separated)", ) beta_btn = gr.Button("Plot β Effect", variant="primary") beta_plot = gr.Plot() beta_desc = gr.Markdown() beta_btn.click(plot_beta_effect, beta_input, [beta_plot, beta_desc]) demo.load(plot_beta_effect, beta_input, [beta_plot, beta_desc]) with gr.Column(): gr.Markdown("#### GAE Lambda — Credit Attribution") lam_btn = gr.Button("Show λ Effect", variant="primary") lam_plot = gr.Plot() lam_btn.click(plot_lambda_effect, [], lam_plot) demo.load(plot_lambda_effect, [], lam_plot) # ── Tab 5: Math Explainer ──────────────────────────────────────────── with gr.TabItem("📐 Mathematics"): gr.Markdown("### Mathematical foundations of UGTC") math_select = gr.Radio( list(MATH_SECTIONS.keys()), value="GAE Foundation", label="Section", ) math_content = gr.Markdown() math_select.change(show_math, math_select, math_content) demo.load(show_math, math_select, math_content) # ── Tab 6: Paper & Citation ────────────────────────────────────────── with gr.TabItem("📄 Paper & Citation"): gr.Markdown(""" ## Paper Information **Title:** UGTC: Uncertainty-Gated Temporal Credit **Author:** Yağız Ekrem Dalar **Status:** Accepted — Ulysseus Young Explorers in Science (UYES) Journal **Preprint DOI:** [10.5281/zenodo.19715116](https://doi.org/10.5281/zenodo.19715116) **Journal DOI:** Forthcoming upon publication --- ## Abstract (from paper) UGTC proposes a backbone-agnostic advantage estimator for actor-critic reinforcement learning. The method maintains two critics with different GAE λ values and blends their estimates using a sigmoid uncertainty gate driven by ensemble disagreement. All hyperparameters (λ_fast=0.80, λ_slow=0.99, M=3, β=5.0, EMA=0.99) are fixed across all benchmarks without per-task tuning. --- ## Citation ```bibtex @misc{dalar2026ugtc, author = {Dalar, Yağız Ekrem}, title = {{UGTC}: Uncertainty-Gated Temporal Credit}, year = {2026}, publisher = {Zenodo}, doi = {10.5281/zenodo.19715116}, url = {https://doi.org/10.5281/zenodo.19715116}, note = {Accepted — Ulysseus Young Explorers in Science (UYES) Journal. Journal DOI forthcoming.} } ``` --- ## Links | Resource | URL | |----------|-----| | Paper (Zenodo) | https://doi.org/10.5281/zenodo.19715116 | | GitHub Repository | https://github.com/ethosoftai/ugtc | | GitHub Pages Docs | https://ethosoftai.github.io/ugtc | | HuggingFace Space | https://huggingface.co/spaces/Ethosoft/ugtc | --- ## Transparency Note This space provides an educational and interactive interface for UGTC. No specific benchmark numbers are reproduced here. All visualizations are based on the described algorithm, not empirical results. Implementation assumptions (e.g., UGTC-DDPG) are labeled as extensions not in the paper. """) if __name__ == "__main__": demo.launch()