{"slug": "reinforce-vs-dqn-learning-policies-directly", "title": "REINFORCE vs DQN: Learning Policies Directly", "summary": "A technical comparison of REINFORCE and DQN reinforcement learning algorithms shows that REINFORCE learns policies directly by outputting action probabilities and sampling, eliminating the need for replay buffers and target networks, but suffers from high variance compared to DQN's lower-variance but biased temporal-difference learning. The author tested REINFORCE in a 4x4 gridworld, highlighting its natural exploration through stochastic sampling and its ability to handle continuous action spaces, while noting that the trade-off between variance and bias motivates hybrid Actor-Critic architectures like PPO.", "body_md": "# REINFORCE vs DQN: Learning Policies Directly\n\nThe technical pivot is simple: instead of the network outputting Q-values and using an `argmax`\n\nto pick the \"best\" action, the network outputs a probability distribution.\n\n```\n# DQN approach\nq_values = network(state) # [2.1, 0.8, 1.4, 3.2]\naction = argmax(q_values) # deterministic choice\n\n# REINFORCE approach\nprobs = network(state) # [0.1, 0.3, 0.2, 0.4]\naction = sample(probs) # stochastic sampling\n```\n\nBy sampling from a distribution, the agent maintains natural exploration. The learning mechanism is a straightforward reward/penalty system: if an action leads to a high total reward, the probability of taking that action again increases.\n\nThe loss function handles this via the log probability of the action taken, scaled by the discounted return:\n\n```\nloss = -G_t * log π(a_t | s_t)\n```\n\nI put this to the test in a 4x4 gridworld. Unlike a DQN setup, this AI workflow requires no replay buffer and no target network because it doesn't rely on Bellman bootstrapping. The training loop is a clean cycle of collecting a full episode and updating parameters based on actual returns:\n\n```\nfor episode in range(3000):\n states, actions, rewards = collect_episode(params, key)\n returns = compute_returns(rewards, gamma=0.99)\n\n loss, grads = grad_fn(params, states, actions, returns)\n updates, opt_state = optimizer.update(grads, opt_state)\n params = optax.apply_updates(params, updates)\n```\n\nComparing the two from a performance standpoint:\n\n**Accuracy:** REINFORCE is Monte Carlo based, meaning it uses actual observed returns. DQN uses estimates (Temporal Difference), which can be biased.**Stability:** REINFORCE suffers from high variance. A single outlier episode can swing the gradients wildly, making it noisier and often slower to converge than DQN.**Versatility:** REINFORCE handles continuous action spaces effortlessly, whereas DQN is locked into discrete sets.\n\nThis trade-off is exactly why Actor-Critic architectures exist—they essentially merge the two, using a value network to stabilize the policy network's variance. This is the fundamental logic that eventually leads to advanced LLM agent optimizations like PPO or GRPO.\n\n[Next LLM Benchmarking: Stop Celebrating 0.000 Scores →](/en/threads/3065/)", "url": "https://wpnews.pro/news/reinforce-vs-dqn-learning-policies-directly", "canonical_source": "https://promptcube3.com/en/threads/3080/", "published_at": "2026-07-25 06:02:18+00:00", "updated_at": "2026-07-25 06:07:55.207209+00:00", "lang": "en", "topics": ["machine-learning"], "entities": ["REINFORCE", "DQN", "Actor-Critic", "PPO", "GRPO"], "alternates": {"html": "https://wpnews.pro/news/reinforce-vs-dqn-learning-policies-directly", "markdown": "https://wpnews.pro/news/reinforce-vs-dqn-learning-policies-directly.md", "text": "https://wpnews.pro/news/reinforce-vs-dqn-learning-policies-directly.txt", "jsonld": "https://wpnews.pro/news/reinforce-vs-dqn-learning-policies-directly.jsonld"}}