This paper proposes a holistic evaluation framework of value-based (Deep Q-Network (DQN), Double DQN), policy-based (Proximal Policy Optimization (PPO), Advantage Actor-Critic (A2C), REINFORCE), and hybrid actor-critic (Soft Actor-Critic (SAC), Twin Delayed Deep Deterministic Policy Gradient (TD3), Deep Deterministic Policy Gradient (DDPG)) RL agents with a wide variety of algorithms. The approaches are evaluated on four Gymnasium environments—CartPolev1, LunarLander-v3, MountainCar-v0, and BipedalWalker-v3—that cover discrete and continuous action spaces with different reward sparsity. For tackling limitations such as poor adaptability, poor exploration, and instability, we propose two new approaches: Reward Prediction Error Adaptive Learning (RPEAL) for adaptive scaling of exploration based on real-time reward prediction error, and Reward-Shaped Adaptive Curriculum Learning (RSACL) for difficulty adaptation based on agent performance. These approaches are congruent with adaptive processes observed in system dynamics modeling, where learning behavior is directed by performance feedback.Large-scale experiments show that hybridizing RPEAL and RSACL strongly enhances stability, sample efficiency, and reward optimization—especially for high-variance and sparse-reward environments. For example, PPO with both techniques enhances mean episodic reward on BipedalWalker-v3 by approximately 7% relative to the PPO baseline. This demonstrates the utility of hybrid adaptive learning for constructing robust, generalizable RL agents. While results are promising, current research is limited to single-agent simulation environments and will be extended to real-world system dynamics and multi-agent environments in the future.