## Line Graphs: Reward vs. Timesteps for DeepQ and PPO2 Baselines ### Overview The image contains two line graphs comparing the performance of two reinforcement learning baselines (`deepq` and `ppo2`) over time. Each graph plots "Reward" against "Timesteps (total)", with distinct trends observed for each baseline. --- ### Components/Axes 1. **Graph (a): The `deepq` baseline** - **X-axis**: "Timesteps (total)" with values from 0 to 5 (scaled by 10⁴, i.e., 0 to 50,000 timesteps). - **Y-axis**: "Reward" ranging from -1 to 0. - **Legend**: Labeled "(a) The `deepq` baseline" at the bottom. - **Line**: Blue, with discrete data points connected by straight lines. 2. **Graph (b): The `ppo2` baseline** - **X-axis**: "Timesteps (total)" with values from 0 to 6,000. - **Y-axis**: "Reward" ranging from -1 to 2. - **Legend**: Labeled "(b) The `ppo2` baseline" at the bottom. - **Line**: Blue, with discrete data points connected by straight lines. --- ### Detailed Analysis #### Graph (a): `deepq` Baseline - **Key Data Points**: - Timestep 0: Reward ≈ -1. - Timestep 1: Reward ≈ 0. - Timestep 2: Reward ≈ 0. - Timestep 3: Reward ≈ -0.5. - Timestep 4: Reward ≈ 0. - Timestep 5: Reward ≈ -0.5. - **Trend**: The reward fluctuates significantly, with sharp increases and decreases. The baseline starts at -1, peaks at 0 (timesteps 1–2), drops to -0.5 (timestep 3), recovers to 0 (timestep 4), and ends at -0.5 (timestep 5). #### Graph (b): `ppo2` Baseline - **Key Data Points**: - Timestep 0: Reward ≈ -1. - Timestep 2,000: Reward ≈ -0.5. - Timestep 4,000: Reward ≈ 1. - Timestep 6,000: Reward ≈ 2. - **Trend**: The reward increases steadily from -1 to 2, with a plateau near 2 after timestep 4,000. The improvement is smooth and consistent compared to `deepq`. --- ### Key Observations 1. **Volatility vs. Stability**: - `deepq` exhibits erratic performance, with rewards oscillating between -1 and 0. - `ppo2` shows a stable, upward trajectory, achieving a reward of 2 by the end of training. 2. **Scaling Differences**: - `deepq` is evaluated over 50,000 timesteps (0–5 × 10⁴), while `ppo2` is evaluated over 6,000 timesteps. This suggests differing training durations or problem complexities. 3. **Performance Gap**: - `ppo2` outperforms `deepq` by a margin of 2.5 (reward of 2 vs. -0.5 at their final timesteps). --- ### Interpretation - **Algorithmic Efficiency**: The `ppo2` baseline demonstrates superior learning stability and optimization, likely due to its policy optimization framework, which reduces variance in rewards. - **DeepQ Limitations**: The `deepq` baseline’s fluctuations may stem from Q-learning’s sensitivity to exploration-exploitation trade-offs or reward sparsity. - **Practical Implications**: `ppo2` is preferable for tasks requiring consistent performance, while `deepq` might be suitable for simpler or less dynamic environments. No additional languages or non-textual elements are present. All data points and labels are explicitly extracted from the graphs.